Multivariate Spectral Analysis and Monitoring for Biomanufacturing

ABSTRACT

The disclosure features methods that include obtaining a vibrational spectrum of a solution in a biological manufacturing system, analyzing the vibrational spectrum using a first chemometrics model to determine a value of a first quality attribute associated with the solution, analyzing the vibrational spectrum using a second chemometrics model to determine a value of a second quality attribute associated with the solution, and adjusting at least one parameter of a purification unit of the biological manufacturing system based on at least one of the values of the first and second quality attributes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to the following U.S. ProvisionalApplications, the entire contents of each of which is incorporatedherein by reference: 62/637,891, filed on Mar. 2, 2018; 62/673,845,filed on May 18, 2018; and 62/729,402, filed on Sep. 10, 2018.

TECHNICAL FIELD

This disclosure relates to systems and methods for use in integrated,continuous bio-manufacturing systems.

BACKGROUND

Mammalian cells containing a nucleic acid that encodes a recombinantprotein are often used to produce therapeutically or commerciallyimportant proteins. Integrated, continuous bio-manufacturing is animportant aspect of reducing costs associated with therapies based onsuch proteins. Monitoring systems are used in bio-manufacturing toassess various biological products and process conditions.

SUMMARY

Integrated, continuous bio-manufacturing of therapeutic proteinsubstances and other biological molecules holds tremendous promise forfuture production of life-saving drugs and enhancing widespread adoptionof therapies that rely on the availability of such biological molecules.Two-column and multi-column chromatography systems in a variety ofconfigurations can be used for bio-manufacturing on an industrial scale.In such systems, process monitoring of various eluent streams can beused to adjust process-related parameters and to control productattributes, for example, the selective collection of eluent streams fromcertain columns and the adjustment of solution buffer properties (e.g.,pH).

This disclosure features methods and systems for determining solutionproperties such as solute concentrations, charge distribution for ananalyte of interest, process and product impurities, molecularintegrity, aggregation state, and pH using real-time or near real-time,sensors that are integrated in-line with chromatography systems and/orbioreactors, and coupled to an electronic controller that analyzesinformation measured by the sensors. Infrared spectra of the solutionscan be monitored continuously, and chemometric models are used toaccurately characterize quantitative chemical, physical, and/orbiological properties of a variety of analytes simultaneously insolution. Spectra can be obtained in-line from flowing solutions so thatmeasurements are performed with little or no disruption to manufacturingprocesses. Further, the chemometric models can extract quantitativeanalyte information in real time or near-real time, permitting rapidfeedback and control over bio-manufacturing process-related parametersand operations.

In a first aspect, the disclosure features methods that includeobtaining a vibrational spectrum of a solution in a biologicalmanufacturing system, analyzing the vibrational spectrum using a firstchemometrics model to determine a value of a first quality attributeassociated with the solution, analyzing the vibrational spectrum using asecond chemometrics model to determine a value of a second qualityattribute associated with the solution, and adjusting at least oneparameter of a purification unit of the biological manufacturing systembased on at least one of the values of the first and second qualityattributes.

Embodiments of the methods can include any one or more of the followingfeatures.

The methods can include using the biological manufacturing system toproduce at least one of a protein-based therapeutic substance, a nucleicacid-based drug substance, and a gene therapy drug substance. Theprotein-based therapeutic substance can include at least one of aprotein, a peptide, an antibody, and an enzyme. The nucleic acid-baseddrug substance can include at least one of DNA, a plasmid, anoligonucleotide, an aptamer, a DNAzyme, an RNA aptamer, an RNA decoy, amicroRNA fragment, and a small interfering RNA fragment.

The first and second quality attributes can each be independentlyselected from the group consisting of product quality attributes,product-related impurities, and process-related impurities, for abiological product produced by the biological manufacturing system.

Obtaining the vibrational spectrum can include directing radiation to beincident on the solution and measuring attenuated totally reflectedradiation from the solution. The radiation can be incident on thesolution by passing through a radiation window, and the attenuatedtotally reflected radiation can pass through the radiation window beforeit is measured. The methods can include measuring the attenuated totallyreflected radiation from the solution while the solution is flowingrelative to the radiation window.

The radiation window can form a portion of a flow cell. The incidentradiation can include infrared radiation.

The first chemometrics model can include a first set of principalvibrational components correlated with the first quality attribute. Thefirst chemometrics model can include at least three principalvibrational components (e.g., at least five principal vibrationalcomponents). Analyzing the vibrational spectrum using the firstchemometrics model can include calculating the first quality attributevalue based on the first set of principal vibrational components.Calculating the first quality attribute value can include determiningthe value as a linear function of the first set of principal vibrationalcomponents.

The second chemometrics model can include a second set of principalvibrational components correlated with the first quality attribute. Thesecond chemometrics model can include at least three principalvibrational components (e.g., at least five principal vibrationalcomponents). The first and second sets of principal vibrationalcomponents can have no members in common. Analyzing the vibrationalspectrum using the second chemometrics model can include calculating thesecond quality attribute value based on the second set of principalvibrational components. Calculating the second quality attribute valuecan include determining the value as a linear function of the second setof principal vibrational components.

The solution can include a solution discharged from a purification unitof the biological manufacturing system. The methods can includepurifying the solution in the purification unit prior to obtaining thevibrational spectrum of the solution. The purification unit can includea chromatography column.

The methods can include obtaining the vibrational spectrum by measuringradiation from the solution as the solution flows between a firstpurification unit and a second purification unit of the biologicalmanufacturing system. The first and second purification units can eachinclude a chromatography column.

The methods can include obtaining the vibrational spectrum by measuringradiation from the solution after the solution flows out of a finalpurification unit of the biological manufacturing system.

The solution can be a first solution, and the methods can includeobtaining a vibrational spectrum of a second solution in the biologicalmanufacturing system, analyzing the vibrational spectrum of the secondsolution using the first chemometrics model to determine a value of thefirst quality attribute for the second solution, and analyzing thevibrational spectrum of the second solution using the secondchemometrics model to determine a value of the second quality attributefor the second solution. The first solution can flow between a firstpurification unit and a second purification unit of the biologicalmanufacturing system, and the second solution can flow between thesecond purification unit and a third purification unit of the biologicalmanufacturing system. The methods can include adjusting the at least oneparameter based on at least one of the first and second qualityattribute values for the first solution, and the first and secondquality attribute values for the second solution.

The methods can include obtaining the vibrational spectrum anddetermining the first and second quality attribute values within a timeperiod of 30 seconds or less, starting from a time at which theradiation is incident on the solution. The time period can be 10 secondsor less (e.g., 2 seconds or less).

The methods can include repeating the steps of obtaining the vibrationalspectrum, analyzing the vibrational spectrum using the firstchemometrics model, and analyzing the vibrational spectrum using thesecond chemometrics model, to determine a temporal sequence of firstquality attribute values and second quality attribute values associatedwith successive portions of the solution. The methods can includeadjusting the at least one parameter based on at least one of thetemporal sequence of first quality attribute values and the temporalsequence of second quality attribute values.

The methods can include obtaining a set of one or more calibrationspectra representative of the solution, and analyzing the set ofcalibration spectra to determine the first and second sets of principalvibrational components. The first chemometrics model can include a firstset of coefficients associated with the first set of principalvibrational components and the second chemometrics model can include asecond set of coefficients associated with the second set of principalvibrational components, and the methods can include determining thefirst and second sets of coefficients based on a regression analysis.

The methods can include analyzing the vibrational spectrum using a thirdchemometrics model to determine a value of a third quality attributeassociated with the solution. The third chemometrics model can include athird set of principal vibrational components correlated with the thirdquality attribute. The third chemometrics model can include at leastthree principal vibrational components (e.g., at least five principalvibrational components). Analyzing the vibrational spectrum using thethird chemometrics model can include calculating the third qualityattribute value based on the third set of principal vibrationalcomponents. Calculating the third quality attribute value can includedetermining the value as a linear function of the third set of principalvibrational components. The methods can include adjusting the at leastone parameter based on at least one of the first, second, and thirdquality attribute values.

The methods can include analyzing the vibrational spectrum using afourth chemometrics model to determine a value of a fourth qualityattribute associated with the solution. The fourth chemometrics modelcan include a fourth set of principal vibrational components correlatedwith the fourth quality attribute. The fourth chemometrics model caninclude at least three principal vibrational components (e.g., at leastfive principal vibrational components). Analyzing the vibrationalspectrum using the fourth chemometrics model can include calculating thevalue of the fourth quality attribute based on the fourth set ofprincipal vibrational components. Calculating the value of the fourthquality attribute can include determining the value as a linear functionof the fourth set of principal vibrational components. The methods caninclude adjusting the at least one parameter based on at least one ofthe first, second, third, and fourth quality attribute values.

At least one of the first and second quality attributes can be selectedfrom the group consisting of concentration, aggregates, charge variantdistribution, purity, glycan profile, identity, and integrity. At leastone of the first and second quality attributes can be selected from thegroup consisting of protein fragments, nucleic acid fragments, nucleicacid variants, empty capsids, and vector impurities. At least one of thefirst and second quality attributes can be selected from the groupconsisting of host cell proteins, residual host DNA, residual columnligands, impurity concentration, impurity amount, residual helper virus,residual helper viral proteins, and residual helper viral DNA.

Embodiments of the methods can also include any of the other featuresdisclosed herein, including combinations of features that areindividually disclosed in connection with different embodiments, exceptas expressly stated otherwise.

In another aspect, the disclosure features biological manufacturingsystems that include a bioreactor configured to produce a solution thatincludes a biological product, a purification unit configured to receivethe solution, a radiation source configured to direct radiation to beincident on the solution, a detection apparatus configured to measuredradiation from the solution, and a system controller connected to thebioreactor and the detection apparatus, and configured to: receive ameasurement signal from the detection apparatus corresponding toinformation about a vibrational spectrum of the solution; analyze theinformation using a first chemometrics model to determine value of afirst quality attribute associated with the solution; analyze theinformation using a second chemometrics model to determine a value of asecond quality attributed associated with the solution; and adjust atleast one parameter of the purification unit based on at least one ofthe values of the first and second quality attributes.

Embodiments of the systems can include any one or more of the followingfeatures.

The systems can include a flow cell positioned so that the solutionpasses through the flow cell, and the radiation source directs theradiation to be incident on the solution while the solution is in theflow cell.

The biological product can include at least one of a protein-basedtherapeutic substance, a nucleic acid-based drug substance, and a genetherapy drug substance. The protein-based therapeutic substance caninclude at least one of a protein, a peptide, an antibody, and anenzyme. The nucleic acid-based drug substance can include at least oneof DNA, a plasmid, an oligonucleotide, an aptamer, a DNAzyme, an RNAaptamer, an RNA decoy, a microRNA fragment, and a small interfering RNAfragment.

The first and second quality attributes can each be independentlyselected from the group consisting of product quality attributes,product-related impurities, and process-related impurities, for abiological product produced by the biological manufacturing system.

The detector can include a total internal reflection sensor configuredto measure attenuated totally reflected radiation from the solution. Thetotal internal reflection sensor can be integrated with a portion of theflow cell. The controller and detection apparatus can be configured tomeasure the attenuated totally reflected radiation from the solutionwhile the solution is flowing within the flow cell. The incidentradiation can include infrared radiation.

The first chemometrics model can include a first set of principalvibrational components correlated with the first quality attribute. Thefirst chemometrics model can include at least three principalvibrational components (e.g., at least five principal vibrationalcomponents).

The controller can be configured to analyze the vibrational spectrumusing the first chemometrics model by calculating the first qualityattribute value based on the first set of principal vibrationalcomponents. The controller can be configured to calculate the firstquality attribute value as a linear function of the first set ofprincipal vibrational components.

The second chemometrics model can include a second set of principalvibrational components correlated with the first quality attribute. Thesecond chemometrics model can include at least three principalvibrational components (e.g., at least five principal vibrationalcomponents). The first and second sets of principal vibrationalcomponents can have no members in common. The controller can beconfigured to analyze the vibrational spectrum using the secondchemometrics model by calculating the second quality attribute valuebased on the second set of principal vibrational components. Thecontroller can be configured to calculate the second quality attributevalue as a linear function of the second set of principal vibrationalcomponents.

The solution can include a solution discharged from the purificationunit. The controller can be connected to the purification unit andconfigured to purify the solution in the purification unit prior toobtaining the information about the vibrational spectrum of thesolution.

The purification unit can include a chromatography column. Thepurification unit can be a first purification unit of the system, andthe system can include a second purification unit configured to receivethe solution, where the detection apparatus is positioned to measureradiation from the solution as the solution flows between the firstpurification unit and the second purification unit. The first and secondpurification units can each include a chromatography column.

The purification unit can be a final purification unit of the system.

The solution can be a first solution, and the detection apparatus can beconfigured to measure radiation from a second solution, and thecontroller can be configured to: receive a measurement signal from thedetection apparatus corresponding to information about a vibrationalspectrum of the second solution; analyze the information about thesecond solution using the first chemometrics model to determine a valueof the first quality attribute for the second solution; and analyze theinformation about the second solution using the second chemometricsmodel to determine a value of the second quality attribute for thesecond solution.

The purification unit can be a first purification unit and the systemcan include a second purification unit and a third purification unit,where the first solution can flow between the first purification unitand the second purification unit, and the second solution can flowbetween the second purification unit and the third purification unit.The controller can be configured to adjust the at least one parameterbased on at least one of the first and second quality attribute valuesfor the first solution, and the first and second quality attributevalues for the second solution.

The detection apparatus and controller can be configured to measure theradiation from the solution and determine the first and second qualityattribute values within a time period of 30 seconds or less, startingfrom a time at which the radiation is incident on the solution. The timeperiod can be 10 seconds or less (e.g., 2 seconds or less).

The detection apparatus and controller can be configured to repeat thesteps of measuring the radiation from the solution, analyzing theinformation about the vibrational spectrum of the solution using thefirst chemometrics model, and analyzing the vibrational spectrum usingthe second chemometrics model, to determine a temporal sequence of firstquality attribute values and second quality attribute values associatedwith successive portions of the solution. The control can be configuredto adjust the at least one parameter based on at least one of thetemporal sequence of first quality attribute values and the temporalsequence of second quality attribute values.

The controller can be configured to obtain a set of one or morecalibration spectra representative of the solution, and to analyze theset of calibration spectra to determine the first and second sets ofprincipal vibrational components. The first chemometrics model caninclude a first set of coefficients associated with the first set ofprincipal vibrational components and the second chemometrics model caninclude a second set of coefficients associated with the second set ofprincipal vibrational components, and the controller can be configuredto determine the first and second sets of coefficients by performing aregression analysis.

The controller can be configured to analyze the information about thevibrational spectrum of the solution using a third chemometrics model todetermine a value of a third quality attribute associated with thesolution. The third chemometrics model can include a third set ofprincipal vibrational components correlated with the third qualityattribute. The third chemometrics model can include at least threeprincipal vibrational components (e.g., at least five principalvibrational components). The controller can be configured to analyze theinformation about the vibrational spectrum using the third chemometricsmodel by calculating the third quality attribute value based on thethird set of principal vibrational components. The controller can beconfigured to calculate the third quality attribute value by determiningthe value as a linear function of the third set of principal vibrationalcomponents. The controller can be configured to adjust the at least oneparameter based on at least one of the first, second, and third qualityattribute values.

The controller can be configured to analyze the information about thevibrational spectrum using a fourth chemometrics model to determine avalue of a fourth quality attribute associated with the solution. Thefourth chemometrics model can include a fourth set of principalvibrational components correlated with the fourth quality attribute. Thefourth chemometrics model can include at least three principalvibrational components (e.g., at least five principal vibrationalcomponents). The controller can be configured to analyze the informationabout the vibrational spectrum using the fourth chemometrics model bycalculating the value of the fourth quality attribute based on thefourth set of principal vibrational components. The controller can beconfigured to calculate the value of the fourth quality attribute bydetermining the value as a linear function of the fourth set ofprincipal vibrational components. The controller can be configured toadjust the at least one parameter based on at least one of the first,second, third, and fourth quality attribute values.

At least one of the first and second quality attributes can be selectedfrom the group consisting of concentration, aggregates, charge variantdistribution, purity, glycan profile, identity, and integrity. At leastone of the first and second quality attributes can be selected from thegroup consisting of protein fragments, nucleic acid fragments, nucleicacid variants, empty capsids, and vector impurities. At least one of thefirst and second quality attributes can be selected from the groupconsisting of host cell proteins, residual host DNA, residual columnligands, impurity concentration, impurity amount, residual helper virus,residual helper viral proteins, and residual helper viral DNA.

Embodiments of the systems can also include any of the other featuresdisclosed herein, including combinations of features that areindividually disclosed in connection with different embodiments, unlessexpressly stated otherwise.

As used herein, “real-time” refers to measurements or processes thatoccur with a relatively small delay or recurrence period. For example,“real-time” measurements are measurements for which a total elapsed timeinterval between the beginning of the measurement of spectroscopicinformation and the time at which a parameter value or other quantity iscalculated from the information is 1 minute or less. Periodic real-timemeasurements are recurring/periodic measurements with a time interval of1 minute or less between successive measurements.

As used herein, “near real-time” measurements are measurements for whicha total elapsed time interval between the beginning of the measurementof spectroscopic information and the time at which a parameter value orother quantity is calculated from the information is between 1 minuteand 5 minutes. Periodic near real-time measurements arerecurring/periodic measurements with a time interval of between 1 minuteand 5 minutes between successive measurements.

As used herein, the term “quality attribute” refers to a value of aparameter that is used to assess the operating condition of abio-manufacturing system, the integrity of a process implemented in sucha system, and/or a product derived from such a process. Qualityattributes can be product quality attributes which relate to the purity,integrity, yield, and other characteristics of a product produced by abio-manufacturing system; they can be product-related impurityparameters which provide information about the product that is producedin the system; and they can be process-related purity parameters whichprovide information about the fidelity of bio-manufacturing processesimplemented in the system.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the subject matter herein, suitable methods andmaterials are described below. All publications, patent applications,patents, and other references mentioned herein are incorporated byreference in their entirety. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram showing an example of a system formeasuring infrared spectral information for a process solution in abio-manufacturing system.

FIG. 2 is a schematic cross-sectional diagram showing an example of afiber.

FIG. 3 a schematic diagram showing an example of a prism that forms anattenuated total reflection interface.

FIG. 4 is a schematic diagram showing an example of a flow cellpositioned between two fluid conduits.

FIG. 5 is a schematic diagram showing another example of a flow cellpositioned between two fluid conduits.

FIG. 6 is a schematic diagram showing a ray of incident radiation thatrefracts at the surface of a prism.

FIG. 7 is a schematic diagram showing an example of a measurement systemthat measures infrared spectroscopic information for a fluid in abio-manufacturing system.

FIG. 8 is a flow chart that shows example steps that can be performed toanalyze vibrational spectroscopic information of a process fluid.

FIG. 9 is a flow chart that shows example steps that can be performed toconstruct and validate a chemometrics model for an attribute of aprocess fluid based on vibrational spectroscopic information measuredfor the process fluid.

FIG. 10 is a schematic diagram showing an example of a bio-manufacturingsystem.

FIG. 11 is a schematic diagram showing an example of a three-columnswitching technique in a periodic counter-current chromatography system.

FIG. 12 is a graph showing a Fourier-transform infrared (FTIR) spectrumof a Protein A purified-antibody sample.

FIG. 13 is a graph showing predicted antibody concentration valuescalculated from a partial least-squares (PLS) model for antibodyconcentration, and corresponding measured antibody concentration values.

FIG. 14A is a graph showing an example of Fourier self-deconvolved FTIRinfrared spectra of Protein A purified samples.

FIG. 14B is a graph showing a PLS calibration model developed foraggregation values for a sample, and measured aggregation values for thesample.

FIG. 15A is a graph showing a set of spectra used to construct a PLSmodel for host cell protein (HCP) content of a sample.

FIG. 15B is a graph showing a PLS model for HCP content of a sampledetermined from the spectra in FIG. 15A, and measured HCP values for thesample.

FIGS. 16A-16C are graphs showing three different PLS models for chargevariant distribution values of a sample.

FIG. 17 is a table showing antibody concentration values predicted forsix samples using a partial least squares model for antibodyconcentration.

FIGS. 18A-18D are graphs showing measurements of viable cell density,cell viability, harvest titer, and specific productivity for a perfusionbioreactor.

FIGS. 19A-19D are graphs showing measurements of glucose concentration,glutamine concentration, lactate concentration, and ammonium ionconcentration for different cell density, pH, and perfusion conditionsfor a bioreactor producing a monoclonal antibody product.

FIGS. 20A-20D are graphs showing daily measurements of glucoseconcentration, harvest titer, lactate concentration, and ammonium ionconcentration for a bioreactor medium.

FIGS. 21A-21F are graphs showing measurements of glucose concentration,glutamine concentration, IgG concentration, lactate concentration,ammonium ion concentration, and osmolarity for a bioreactor mediumdetermined from infrared spectral information.

FIGS. 22A-22D are graphs showing multiple values of glucoseconcentration, harvest titer, lactate concentration, and ammonium ionconcentration determined for a bioreactor medium from infrared spectralinformation.

FIGS. 23A-23C are graphs showing multiple values of glucoseconcentration, lactate concentration, and ammonium ion concentration atlater stages of a bioreactor culture medium, determined from infraredspectral information.

Like symbols in the drawings indicate like elements.

DETAILED DESCRIPTION Introduction

Industrial scale bio-manufacturing can be performed in two-column andmulti-column chromatography systems in a variety of configurations. Inthese complex systems, product yield, quality, and waste rates arefunctions of a large number of process-related parameters and steps.During manufacturing of therapeutic proteins and other commerciallyvaluable bio-molecules, product outcomes can be strongly influenced bythese parameters and steps. Appropriate control over such parameters andsteps is therefore an important aspect of large scale manufacturing.Features and aspects of bio-manufacturing systems are disclosed, forexample, in PCT Patent Application Publication No. WO 2014/137903, theentire contents of which are incorporated herein by reference.

Exercising appropriate control over bio-manufacturing parameters,including automated control, is facilitated by in-process, in-linemonitoring of intermediate solution streams, and specifically,concentrations of intermediates and products in such streams, and otherproperties (such as pH, for example) of such streams. Conventionalmethods for solution monitoring include techniques such as UV absorbancemeasurements. Unfortunately, however, such methods are subject to driftover measurement periods of a few days due to factors such astemperature, humidity, ambient light intensities, and local sampleinhomogeneity.

Furthermore, such methods typically do not allow multiple quantities tobe calculated or otherwise determined in real-time or near real-timefrom a single measurement (e.g., a measurement of an absorbancespectrum). Instead, there is generally a 1:1 correspondence betweenmeasurements performed and quantities determined from such measurements.Thus, for example, to measure concentrations of two different species ina solution stream, two different UV absorbance measurements would berecorded, and each would yield a concentration value for one of thespecies. Due to the measurement and analysis time required to determinesuch values, it may not be possible to determine values of multiplequantities in real-time or near real-time.

Disclosed herein are methods and systems that use infrared spectroscopicmeasurements in combination with multivariate chemometric models todetermine quantitative information about analytes and properties inprocess solutions. The analytes can include product and intermediatecomponents, waste products, residual reagents, and buffer components,for example. Examples of properties can include, but are not limited to,pH levels, salinity levels, protein/peptide aggregation levels,quantities/concentrations of biological products such as proteins,peptides, and nucleic acids, concentrations/quantities of process andproduct impurities, and charge variant distributions of proteins.

Infrared spectroscopic measurements can be performed rapidly and withhigh reproducibility, and calibrated chemometric models are used topredict quality attributes in real-time or near real-time under avariety of biomanufacturing process conditions, and for a variety ofreagents, products, by-products, and impurities. The extensive controlafforded by the chemometric-based analysis methods disclosed hereinallows biomanufacturing processes to be performed more efficiently, withless waste, higher product yields, and greater product purity.

Due to the wavelengths of infrared radiation, infrared spectroscopicmeasurements generally provide information about vibrational propertiesof analytes such as reaction products and by-products in process fluidswithin biomanufacturing systems. While other measurement modalities suchas Raman spectroscopy can also yield important information about suchanalytes, infrared measurements can provide certain advantages in somecircumstances. Typically, for example, measurements of infraredspectroscopic information can be performed more quickly (e.g., inapproximately 30 s or less) than corresponding Raman measurements, whichcan take between 10 and 15 minutes. Accordingly, infrared measurementscan be better suited for real-time and near real-time process monitoringand control applications.

The application of infrared measurements to upstream and downstreamprocess monitoring in biomanufacturing operations for biological drugshas been very limited to-date due to the implementation of manufacturingprocesses in aqueous solution, and the strong absorption of water atapproximately 1640 cm⁻¹. Process water essentially interferes withconventional analytical calculations based on infrared measurements,sometimes to the extent that determination of quantitative informationfrom such measurements is not possible. However, the chemometric methodsdisclosed herein can reliably predict quantitative values of a varietyof parameters even in the presence of strong water absorption. As such,the methods disclosed herein can use infrared spectral measurements forroutine, high-throughput process monitoring and control.

In addition, infrared spectroscopic measurements can be more sensitiveto changes in analytes and properties of process fluids than Ramanmeasurements. Combined with the rate at which such measurements can beperformed, infrared spectroscopic measurements may be better suitable tohigher throughput applications during active monitoring ofbiomanufacturing processes.

Further, infrared spectroscopic measurements are generally lesssensitive to variations in environmental conditions that occur as themeasurements are performed. Relative to Raman measurements, for example,infrared spectroscopic measurements are typically less sensitive tovariations in temperature, humidity, and ambient light. Accordingly,chemometric models based on such measurements—once calibrated—can beused in a greater variety of conditions, and may be easier to transferfrom a laboratory to a commercial, high-throughput manufacturingoperation.

Infrared Spectroscopic Measurements and Measurement Systems

FIG. 1 is a schematic diagram showing an embodiment of a measurementsystem 100 for measuring infrared spectral information for a processsolution in a biomanufacturing system. System 100 includes a flow cell106 positioned between fluid conduits 102 and 104. A fluid 122 entersflow cell 106 from conduit 102, flows through cell 106 in the directionshown by the arrows in FIG. 1, and exits cell 106 into conduit 104.

In some embodiments, as shown in FIG. 1, flow cell 106 includes anattenuated total reflection (ATR) interface for measuring infraredspectral information in an ATR configuration/mode. In FIG. 1, the ATRinterface is implemented as a prism 108 that forms a portion of aninterior cavity within cell 106. A first fiber 110 is optically coupledto a first surface 124 of prism 108 and to a light source 112. A secondfiber 114 is optically coupled to a second surface 126 of prism 108 andto a detector 116. Radiation source 112 and detector 116 areelectrically connected to a controller 120.

During operation, controller 120 activates radiation source 112 togenerate incident radiation 130. Incident radiation 130 is coupled intofirst fiber 110 and delivered by first fiber 110 into prism 108 throughfirst surface 124. After entering prism 108, incident radiation 130propagates toward surface 128 of flow cell 106.

The angle of incidence θ of incident radiation 130 with respect to thenormal to surface 128, and the material from which flow cell 106 isformed, are selected such that when incident radiation 130 reachessurface 128, the incident radiation undergoes total internal reflectionat surface 128, generating reflected radiation 132. Reflected radiation132 is coupled into second fiber 114 through surface 126, propagatesthrough second fiber 114, and is detected by detector 116. Detector 116converts reflected radiation 132 into spectral information and transmitsthe spectral information to controller 120. As will be explained ingreater detail below, controller 120 analyzes the spectral informationto determine information about one or more components of fluid 122and/or one or more process conditions associated with a biomanufacturingprocess that produces fluid 122.

Radiation source 112 can include a variety of different sources ofradiation. In some embodiments, for example, radiation source 112includes one or more light emitting diodes (LEDs). In general, radiationsource 112 generates incident radiation 130 in the infrared region ofthe electromagnetic spectrum. For example, incident radiation 130typically includes radiation at one or more wavelengths between 750 nmand 500 microns.

In certain embodiments, radiation source 112 generates incidentradiation 130 at multiple, distinct wavelengths. For example, radiationsource 112 can generate incident radiation 130 at 3 or more (e.g., 5 ormore, 7 or more, 10 or more, 15 or more, 20 or more, or even more)distinct, spectrally separated “bands” or “lines”, each of which has acentral wavelength within the infrared region of the spectrum.

In some embodiments, radiation source 112 generates broadband incidentradiation 130 over a range of wavelengths in the infrared spectralregion. For example, incident radiation 130 can have a full-width athalf-maximum bandwidth of 100 nm or more (e.g., 200 nm or more, 500 nmor more, 1 micron or more, 5 microns or more, 10 microns or more, 50microns or more, 100 microns or more, 200 microns or more, 300 micronsor more).

Detector 116 can generally be implemented in a variety of ways. Incertain embodiments, for example, detector 116 can be a Fouriertransform infrared (FTIR) spectrometer that receives reflected radiation132 and generates spectral information for the reflected radiation. Onesuitable FTIR spectrometer for use as detector 116 is the BrukerMATRIX-MF® FTIR (available from Bruker Optics, Billerica, Mass.), with amercury cadmium telluride (MCT) infrared sensor, although many otherFTIR spectrometers can also be used.

Detector 116 can also be implemented using standard optical elementsthat spatially disperse the frequency components of reflected radiation132, thereby mapping radiation frequency onto a spatial coordinatedirection, and then analyzing the dispersed frequency components as afunction of position to measure radiation intensity. Suitable opticalelements for spatially dispersing the frequency components of reflectedradiation 132 include gratings, prisms, diffractive optical elements,and adaptive modulators such as liquid crystal-based optical modulators.After the frequency components of reflected radiation 132 have beenspatially dispersed, the radiation intensity as a function of frequencycan be measured using various detectors suitable for use in detectinginfrared radiation, including detectors based on one or more of mercurycadmium telluride, indium antimonide, indium arsenide, and leadselenide, and/or detectors featuring sensors based on quantum wellsand/or quantum dots.

As shown in FIG. 1, in some embodiments, incident radiation 130 andreflected radiation 132 can be delivered to and from the ATR interface(e.g., prism 108) through optical fibers 110 and 114. In certainembodiments, however, both incident radiation 130 and reflectedradiation can be delivered to and from the ATR interface using a singlefiber. FIG. 2 shows a schematic cross-sectional diagram of a fiber 200that includes a first radiation transporting core 210 and a secondradiation transporting core 214, surrounded by an opaque claddingmaterial 252. Integrated into one end of fiber 200 is an ATR interface(implemented as a prism 208). Prism 208 is positioned so that surface250 can be optically coupled to flow cell 106 of FIG. 1, in place ofprism 108.

During operation, incident radiation 130 generated by radiation source112 is coupled into fiber core 210 and propagates through core 210,reaching integrated prism 208. The incident radiation enters prism 208and undergoes total internal reflection from surface 250 of prism 208,generating reflected radiation 132. Reflected radiation 132 is coupledinto core 214 and propagates back through fiber 200. At the end of fiber200 opposite to prism 208, reflected radiation 132 is coupled intodetector 116 and analyzed as discussed above.

In general, the foregoing examples represent only a subset of theoptical fibers and probes that can be used to transport incidentradiation 130 and reflected radiation 132 in system 100. A wide varietyof fibers and probes can be used, including certain commerciallyavailable components. One example of a suitable commercially availableprobe is the IN350T® diamond ATR probe (available from Bruker Optics).

In addition, in some embodiments, incident radiation 130 and/orreflected radiation 132 can propagate through free space rather thanbeing transported in optical fibers. Free-space propagation can permitadditional optical elements such as filters, apertures, beam splitters,mirrors, and lenses to be inserted into the optical path of either orboth incident radiation 130 and reflected radiation 132, permittingfurther control over the properties of both beams.

As shown in FIGS. 1 and 2, in some embodiments, the ATR interface can beimplemented as a prism with geometrical features (e.g., apex angle)selected to ensure that total internal reflection of incident radiation130 occurs when the incident radiation encounters the interface betweenthe prism material and fluid 122. More generally, the ATR interface canbe implemented in other ways as well. FIG. 3 is a schematic diagramshowing another example of an ATR interface, implemented as a trapezoid308 (or alternatively, as a truncated prism). Surface 328 of trapezoid308 forms a portion of an interior cavity of flow cell 106. In addition,a reflective coating 362 is disposed on surface 360 of trapezoid 308,opposite to surface 328.

During operation, incident radiation 330 is coupled into trapezoid 308through surface 324 (e.g., from an optical fiber). Incident radiation330 is incident at a first location 372 along the interface betweenfluid 122 and surface 328 of trapezoid 308. The incident radiationundergoes total internal reflection at surface 328, forming reflectedradiation 332. Reflected radiation 332 is reflected by coating 362 onsurface 360, and is incident again at location 374 along the interfacebetween fluid 122 and surface 328 of trapezoid 308. Reflected radiation334 emerges from location 374, is reflected by coating 362, and isincident at location 376 along the interface between fluid 122 andsurface 328. Reflected radiation 336 emerges from location 376 and iscoupled out of trapezoid 308 through surface 326 (e.g., into an opticalfiber).

The geometrical features of trapezoid 308 ensure that incident radiation330 interacts multiple times with fluid 122 through surface 328 oftrapezoid 308, increasing the signal-to-noise ratio of the spectralinformation carried by reflected radiation 336, relative to thesingle-interaction measurements that occur for example in FIG. 1. Ingeneral, trapezoid 308 can be configured so that the number ofinteractions between the incident radiation 330 and fluid 122 throughsurface 328 is 2 or more (e.g., 3 or more, 5 or more, 7 or more, 10 ormore, or even more).

In general, ATR interfaces such as prism 108 and trapezoid 308 for usein system 100 are formed from relatively high refractive index materialsto ensure that total internal reflection occurs at the interface betweenprism 108 and/or trapezoid 308 and fluid 122 (e.g., surface 128 and/or328). Suitable materials from which ATR interfaces such as prism 108and/or trapezoid 328 can be formed include, but are not limited to,diamond, germanium, various thallium halides, zinc selenide, andsilicon.

In FIG. 1, flow cell 106 is implemented as a flow-through cell connectedbetween conduits 102 and 104. Fluid 122—which can include any one ormore of a variety of different process fluids at various locationswithin a biomanufacturing system—flows through cell 106 as infraredspectral information is measured and analyzed. Flow cell 106 can bepositioned at a variety of locations within a biomanufacturing system toallow measurement of spectral information for purposes of productquality assessment and process control. For example, in someembodiments, flow cell 106 can be positioned at an outlet port of abioreactor (e.g., a perfusion bioreactor such as a tangential flowperfusion bioreactor). Alternatively, or in addition, flow cell 106 canbe positioned along a fluid flow path between components of abiomanufacturing system, including between a bioreactor and apurification unit, between two purification units, and/or after apurification units. Biomanufacturing systems can generally include asingle flow cell 106 or multiple flow cells 106, each of which ispositioned within the biomanufacturing system to measure spectralinformation at a different location.

In some embodiments, infrared spectral measurements are performed whilefluid 122 is not flowing in cell 106. FIG. 4 is a schematic diagramshowing a flow cell 106 positioned between two fluid conduits 102 and104. A first valve 402, electrically connected to controller 120, ispositioned between conduit 102 and flow cell 106. A second valve 404,electrically connected to controller 120, is positioned between flowcell 106 and conduit 104. During operation, controller 120 opens valve402 to admit a portion of fluid 122 into flow cell 106. When the portionof fluid 122 has entered flow cell 106, controller 120 closes valve 402.With valve 404 remaining closed, the portion of fluid 122 is temporarilytrapped within flow cell 106. Infrared spectral information for fluid122 is measured by controller 120 as discussed above with fluid 122remaining static within flow cell 106, and then controller 120 opensvalve 404 to allow the portion of fluid 122 to flow out of cell 106. Atthe same time or later, controller 120 can also open valve 402 to admita new portion of fluid 122 into flow cell 106. In this manner, infraredspectral information can be measured from a non-flowing portion of fluid122, which can be useful when the flow rate of fluid 122 through cell106 would otherwise perturb or interfere with obtaining accurate,reproducible infrared spectral information from the fluid. Suchsituations can arise, for example, when fluid flow through cell 106would be highly turbid, leading to scattering of incident radiation 130and/or interactions with bubbles and other flow-related inhomogenietiesin fluid 122.

While FIG. 1 shows one example of a flow cell 106 that can be used insystem 100, more generally flow cells having a variety of differentgeometries can be used. FIG. 5 is a schematic diagram showing a flowcell 506 positioned between conduits 102 and 104. An ATR interface formsa portion of a cavity or channel wall within flow cell 506. As shown inFIG. 5, fluid 122 flows from conduit 102 through cell 506 and intoconduit 104. Infrared spectral measurements are performed via ATR ofincident radiation 130 at interface 128 while fluid 122 is flowing, oralternatively, while fluid 122 is static within flow cell 506 (e.g.,when valves connected to controller 120 adjust the flow of fluid 122into and out of flow cell 506, as discussed above.

While flow cell 106 in FIG. 1 defines a non-linear, two-dimensionalfluid flow path from conduit 102 through flow cell 106 and into conduit104, flow cell 506 defines a linear, one-dimension fluid flow path dueto the positions of fluid ports 502 and 504 at either end of flow cell506. For process fluids that flow at relatively higher rates within abiomanufacturing system, flow cell 506 may provide certain advantagesrelative to flow cell 106. In particular, flow cell 506 may allow ahigher aggregate fluid flow rate to be maintained, thereby ensuring thatinfrared spectral measurements do not introduce a flow-rate bottleneckinto a continuous biomanufacturing process.

As discussed above, certain measurement systems disclosed herein areconfigured to perform infrared spectroscopic measurements using anATR-based geometry. The ATR geometry takes advantage of a mismatch inrefractive indices between the material from which the ATR interface(e.g., prisms 108 and 308) is formed and fluid 122. FIG. 6 is aschematic diagram showing a ray 602 of incident radiation 130 that isincident on surface 128 of prism 108. Surface 128 forms the boundarybetween the material from which prism 108 is formed (generally, amaterial having a relatively high index of refraction at the wavelengthof ray 602) and fluid 122 (which generally has a comparatively smallerindex of refraction). Snell's law describes the relationship between theangles of incidence and reflection at the interface, θ_(i) and θ_(r),and the indices of refraction n_(p) and n_(f), of the prism and fluid,respectively:

n _(p) sin θ_(i) =n _(f) sin θ_(r)  [1]

As the value of n_(p) increases relative to the value of n_(f), thevalue of sin θ_(r) increases to maintain the relationship in Equation(1). In other words, the larger the refractive index mismatch betweenprism 108 and fluid 122, the larger the value of sin θ_(r), which meansthat the angle of refraction θ_(r) increases. For fixed values of n_(p)and n_(f), the angle of incidence θ_(i) can be selected such that sinθ_(r)=1. That is, the refracted ray 604 propagates in a directiontangential to surface 128. This angle of incidence is referred to as thecritical angle, θ_(c).

For angles of incidence larger than the critical angle, no refracted ray604 is generated at surface 128. Instead, incident ray 602 undergoestotal internal refraction at surface 128, producing a reflected ray 606.However, at surface 128, an evanescent field extends for a shortdistance—often referred to as a penetration depth—beyond the interfaceand into fluid 122. No energy flows across the interface. Nonetheless,the evanescent field interacts with fluid 122 (and components of thefluid) and the spectral properties of the field are modified by thisinteraction. As a result, by analyzing variations in the spectralproperties of reflected ray 606, information about various components(such as analytes and manufacturing by-products) and fluid attributescan be extracted.

In some embodiments, the measurement of infrared spectroscopicinformation using ATR geometry can provide certain advantages. Forexample, relative to transmission-mode infrared spectroscopicmeasurements, the ATR geometry involves a penetration depth by theevanescent field into the fluid that is interrogated that is relativelyshort compared to conventional absorptive path lengths used intransmission-mode experiments. In general, the longer the path length,the greater the reduction in sensitivity of the measurement. Thus, incertain embodiments, ATR-based infrared spectroscopic measurements canbe performed at higher sensitivity than transmission-mode infraredabsorption measurements.

Nonetheless, in some embodiments, chemometrics-based analysis can beperformed on infrared spectroscopic information acquired viatransmission-mode measurements. FIG. 7 shows a schematic diagram of ameasurement system 700 for use with a biomanufacturing system. System700 measures infrared spectroscopic information for fluid 122 via atransmission-mode measurement geometry.

Fluid 122 flows from conduit 102 through flow cell 106 and into conduit104. Controller 120 activates radiation source 112 which generatesincident radiation 130. Incident radiation 130 propagates through fiber110 and is coupled through a window that forms a portion of a wall offlow cell 106 and into an interior cavity or channel within flow cell106. Once inside the flow cell, at least a portion of incident radiation130 is absorbed by one or more analytes or other components of fluid122. The non-absorbed portion of incident radiation 130 emerges througha second window in flow cell 106 as transmitted radiation 702, andpropagates through second fiber 114 to detector 116. Electrical signalsencoding the infrared spectroscopic information carried by transmittedradiation 602, generated by detector 116, are transmitted to controller120 for analysis.

Chemometrics-Based Analysis of Infrared Spectroscopic Information

Predictive, chemometrics model-based analysis of spectroscopicmeasurements for purposes of process analytics in the manufacturing ofsmall-molecule pharmaceutical substances has enjoyed some success.However, such methods have not been applied to the development of largerbio-therapeutics such as antibody-based drugs because such products arelarge in size, complex, and heterogeneous. Further, biomanufacturingprocesses for such substances are more complex than for small-moleculepharmaceuticals, and yield process solutions containing a wide range ofanalytes and other substances. To-date, these complexities havecounseled against the application of chemometrics methods to processcontrol for the biomanufacturing of large biological molecules such asantibody-based pharmaceuticals and nucleic acid-based products.

However, it has been discovered that despite the foregoingcomplications, chemometrics model-based analysis of spectroscopicmeasurements can yield values of a variety of process-related parametersand product attributes associated with the biomanufacturing of largebiological molecules. These parameters and attributes can be used toprocess feedback and control in real time or near real-time. Inparticular, vibrational spectroscopic information derived from infraredreflectance (or absorption) measurements is particularly amenable tochemometrics model-based analysis, as the spectroscopic informationprovides a rich data set with prominent features such as stretching andbending resonances that are directly associated with particular analytesand by-products. More generally, the spectroscopic information encodes acomplex set of responses of process fluid components to incidentinfrared radiation, and multivariate data analysis tools can decodethese responses and, for certain parameters, be used predictively withhigh accuracy.

In this section, methods and systems for constructing and applyingchemometrics models to infrared spectroscopic information are discussed.The methods and systems are particularly well-suited for generatingvalues of a variety of product attributes and parameters associated withprocess fluids generated during continuous biomanufacturing operations,and can be used to monitor such operations and perform process controlvia adjustment of various biomanufacturing process inputs andproperties. In particular, the methods can be used for biomanufacturingprocess feedback and control in real time or near-real time.

As will be described in greater detail below, the multivariate dataanalysis methods discussed herein are used to isolate responses inspectral information that are attributable to different analytes andquality attributes. After chemometric models have been constructed foreach analyte and/or quality attribute from data obtained from areference method (such as chromatography or mass spectrometry), then asingle set of spectral information (e.g., an infrared absorbance orreflectance spectrum) can be obtained, and the spectral information canbe processed using the chemometric models to predict quantitative valuesof each of the quality attributes corresponding to the models from thesame set of spectral information. Because the models operate on the sameset of information, predictive determination of the values of thequality attributes can occur in real-time or near real-time.

The chemometrics model-based analytical methods discussed in thissection operate on infrared spectroscopic information to determinevalues of process-related parameters. The infrared spectroscopicinformation can be measured as described in the previous section, or byusing other methods. Typically, the infrared spectroscopic informationcorresponds to vibrational spectroscopic information, and moreparticularly, to a vibrational spectrum of a process fluid (and thecomponents contained within the fluid). A vibrational spectrum is atwo-dimensional data set that includes intensity values as a function ofradiation wavelength or frequency, where the range of wavelengths orfrequencies falls within the infrared portion of the electromagneticspectrum and therefore corresponds to typical wavelengths or frequenciesof different vibrational modes of components of the process fluid.

More generally, as used herein, vibrational spectroscopic informationincludes any data set that represents vibrational responses (e.g.,different vibrational modes) of components of a process fluid. Thevibrational spectroscopic information can be encoded as a conventionalvibrational spectrum, or in a different form which represents similarinformation content to a vibrational spectrum.

FIG. 8 is a flow chart 800 that shows a series of example steps that canbe performed to analyze vibrational spectroscopic information (such as avibrational spectrum) of a process fluid and its associated componentsto determine values of various attributes of the fluid and itsassociated components. Each of the steps shown in flow chart 800 can beexecuted by a system controller, such as controller 120, in automatedfashion to perform the analysis.

In a first step 802, calibrated and validated chemometrics models foreach of the attribute values to be determined are obtained. In someembodiments, the chemometrics models—which typically consist ofcalibrated coefficients describing a functional relationship between thevalue of an attribute of the process fluid and spectroscopic intensityvalues at one or more wavelengths or frequencies—can be obtained byretrieving previously stored values of the coefficients from a storagemedium. Alternatively, in certain embodiments, the chemometrics modelsare constructed and validated prior to being used predictively bycontroller 120.

FIG. 9 is a flow chart that shows a series of example steps that can beperformed to construct and validate a chemometrics model for anattribute of a process fluid based on vibrational spectroscopicinformation measured for the process fluid. In a first step 902,controller 120 determines a set of characteristic model independentvariables. In practice, the set of model variables corresponds to theset of independent vibrational frequencies within the vibrationalspectroscopic information that are predictive of the attribute for whichthe model is constructed. It should be understood that while thefollowing discussion refers to “frequencies” within the vibrationalspectroscopic information, the discussion could also equivalently referto “wavelengths” given the reciprocal relationship between wavelengthand frequency in vibrational spectroscopic information. That is, methodsin which chemometrics models are defined in terms of a set offrequencies are equivalent to methods in which chemometrics models aredefined in terms of a set of wavelengths.

The set of independent vibrational frequencies can be determined in avariety of ways. For example, in some embodiments, the set ofvibrational frequencies can be determined by performing a principalcomponents analysis on multiple calibration data sets, each of whichcorresponds to vibrational spectroscopic information related to aprocess fluid with a different, known value of the attribute ofinterest. The principal components analysis determines how manyindependent frequencies are reliably predictive of the attribute valueacross each of the calibration data sets, and the values of theindependent frequencies. The set of principal components corresponds tothe set of frequencies that form the independent model variables in step902. Methods for performing principal components analysis are discussed,for example, in Bro et al., “Principal component analysis,” Anal.Methods 6:2812-2831 (2014), and in Chatfield et al., “Principalcomponent analysis,” in Introduction to Multivariate Analysis, Springer,pp. 51-87 (1980), the entire contents of each of which are incorporatedherein by reference.

Next, in step 904, the chemometrics model for the attribute of interestis constructed based on the set of independent variables from step 902.In general, chemometrics models can take a variety of functional forms.One such form is a linear model in which the value of the attribute ofinterest, A, is expressed as a linear function of the set of independentvariables:

A=a ₁ I _(v1) +a ₂ I _(v2) + . . . +a _(i) I _(vi) + . . . +a _(n) I_(vn)  [2]

where v₁ . . . v_(n) are a set of n frequencies corresponding to the setof independent variables determined in step 902 (i.e., a set of nprincipal components), and I_(v1) . . . I_(vn) are a set of n intensityvalues from the vibrational spectroscopic information corresponding toeach of the n frequencies. The parameters a₁ . . . a_(n) are a set ofcoefficients that effectively weight the contribution of each intensityvalue from the vibrational spectroscopic information to the predictionof the attribute value, A.

In general, as shown by Equation (2), the chemometrics model for thevalue of attribute A is a multivariate model in which the value ofattribute A depends on multiple independent variable values. The numberof independent variables in the chemometrics model can generally beselected as desired based on, for example, the results of a principalcomponents analysis. Thus, for example, the value of attribute A in thechemometrics model can be expressed as a function of one or more (e.g.,two or more, three or more, four or more, five or more, six or more,seven or more, eight or more, ten or more, 12 or more, 15 or more, 20 ormore, or even more) independent variables.

In some embodiments, chemometrics models have a more complex form thanthe linear form of Equation (2). For example, certain chemometricsmodels can be non-linear, in which the value of the attribute ofinterest, A, is expressed as a non-linear function of the set ofindependent variables. Such functional forms for the chemometric modelcan be expressed as follows:

A=f ₁(I _(v1))+f ₂(I _(v2))+ . . . +f _(i)(I _(vi))+ . . . +f _(n)(I_(vn))  [3]

where each of the functional forms, f_(j)(I_(vj)), is linear ornon-linear in I_(vj). For Equation (3) to represent a nonlinearchemometric model for A, at least one of the f_(j)(I_(vj)) is anonlinear function of I_(vj).

In general, each of the f_(j)(I_(vj)) functional forms can be linear ornon-linear in I_(vj). Where f_(j)(I_(vj)) is a non-linear function ofI_(vj), f_(j)(I_(vj)) can have any of a variety of different forms suchas, but not limited to, an exponential form, a logarithmic form, apolynomial form, a power law form, a trigonometric form, a hyperbolicform, and any combination of any of the foregoing functional forms. Suchfunctional forms can generally be selected as desired duringconstruction of the chemometric models to ensure that the predictivecapabilities of the models for values of attribute A are sufficientlyaccurate. The subsequent discussion focuses on the linear example ofEquation (2), but it should be appreciated that similar principles applyto non-linear chemometrics models as well. That is, non-linearchemometrics models—like linear chemometrics models—have coefficientsthat are determined from calibration data sets, and are then used topredict quality attribute values from measured vibrational (i.e.,infrared) spectroscopic information.

Returning to the linear model of Equation (2), in the next step 906,after the model has been constructed, the coefficient values aredetermined based on one or more calibration data sets. The followingdiscussion assumes that chemometrics model for attribute A is a linearmodel defined according to Equation (2) above. If the model isdifferent, the methods discussed below can be used with slightmodifications to determine the coefficient values.

Each calibration data set corresponds to a set of vibrational spectralinformation (e.g., a vibrational spectrum) for a process fluid with adifferent, known value of attribute A. Referring to Equation (2), foreach calibration data set, the values of A and I_(v1) . . . I_(vn) areknown. Thus, in step 906, a single set of coefficient values a₁ . . .a_(n) are determined in self-consistent fashion across each calibrationdata set so that Equation (2) determines, as correctly as possible, eachknown value of attribute A for each calibration data set.

Various methods can be used to determine the set of coefficient valuesa₁ . . . a_(n) across all calibration data sets. In some embodiments,for example, partial least-squares regression analysis is usedsimultaneously across all calibration data sets, minimizing the sum ofsquared error terms for each value of attribute A. Methods forperforming partial least-squares regression are described, for example,in Haenlein et al., “A Beginner's Guide to Partial Least SquaresAnalysis,” Understanding Statistics 3(4):283-297 (2004), and in Sellin,“Partial Least Squares Analysis,” Int. J. Educational Research 10(2):189-200 (1986), the entire contents of each of which are incorporatedherein by reference.

Next, in step 908, the set of coefficient values a₁ . . . a_(n)determined in step 906 can optionally be validated against one or moreadditional calibration data sets to verify that the chemometrics modelfor attribute A predicts attribute values to within an acceptable error.This validation step involves using the chemometrics model to predictone or more values of attribute A for a process fluid based on one ormore sets of calibration data (each of which is a set of vibrationalspectroscopic information) for the process fluid. Since the value ofattribute A is known for each process fluid for which a calibration dataset is measured, the accuracy of predicted values of attribute Agenerated by the chemometrics model can readily be assessed.

In optional step 910, the set of coefficient values a₁ . . . a_(n) canbe stored in a storage medium for later retrieval by controller 120. Theprocess shown in flow chart 900 ends at step 912.

In some embodiments, both the calibration data set(s) and the measuredvibrational spectroscopic information can be processed prior to use inconstructing chemometrics models and/or generating predictive qualityattribute values from the measured information. In general, a variety ofprocessing steps can be implemented. In some embodiments, for example,calibration data set(s) and/or measured spectroscopic information can bebaseline-corrected. In certain embodiments, processing steps such asmean normalization and/or derivatization can be performed on the datasets(s) and/or measured information. Such steps can be performed, forexample, using commercial analytical software such as MATLAB® (availablefrom MathWorks, Natick, Mass.).

In certain embodiments, a chemometrics model that is predictive forvalues of an attribute A may also be specific to certain conditionsunder which spectroscopic information is measured. For example, themodel may be tied to a particular process fluid in which the informationis measured (e.g., a fluid eluting from a particular chromatographycolumn). Accordingly, in some embodiments, the methods disclosed hereininclude the generation of more than one chemometrics model forpredicting values of attribute A.

As an example, a first chemometrics model may be generated to predict aconcentration of A in a process fluid eluting from a firstchromatography column in a biomanufacturing system, and a secondchemometrics model may be generated to predict a concentration of A in adifferent process fluid eluting from a second chromatography columndownstream from the first column. Due to the different compositions ofthe fluids eluting from the two columns, the chemometrics models can bedifferent, but each is specifically generated to accurately predictvalues of attribute A at a certain measurement location in the system.Where multiple models for attribute A are generated, each of the modelscan optionally be stored.

Returning to FIG. 8, after calibrated chemometrics models have beenobtained for each attribute of interest, vibrational spectroscopicinformation for a process fluid is obtained in step 804. As an example,infrared reflectance measurements using the ATR geometry—as discussedabove—can be used to obtain the vibrational spectroscopic information,which can correspond to a vibrational spectrum of the process fluid andits associated components.

Next, in step 806, the vibrational spectroscopic information is analyzedusing a first chemometrics model obtained in step 802 to determine avalue of a first attribute of interest for the fluid. As discussed abovein connection with flow chart 900, determining the value of eachattribute A involves calculating the value of A using the measured setof intensity values I_(v1) . . . I_(vn) at each of the independentfrequency variables from the vibrational spectroscopic information, andthe set of coefficients a₁ . . . a_(n) determined for the chemometricsmodel. Because the calculation is deterministic (e.g., whether thechemometrics model is linear as in Equation (2) or more complex), it isperformed very rapidly by controller 120.

After the value of the first attribute is determined, control passes todecision step 808. If additional attribute values for the process fluidare to be determined, then the chemometrics model for the next attributeof interest is selected in step 812, and control returns to step 806,where the value of the next attribute value of interest is determined.Where a total of m attribute values are determined for a process fluid,m different chemometrics models are applied to the vibrationalspectroscopic information in step 806.

A significant advantage of the methods and systems disclosed herein isthat, due to the richness of the vibrational spectroscopic information,each of the m attribute values can be obtained from the same set ofvibrational spectroscopic information. That is, new measurements are notperformed to determine each attribute value. Instead, the vibrationalspectroscopic information is measured only once, and multiple attributevalues—each of which can be determined based on a set of multipleindependent variables—are determined from the same set of vibrationalspectroscopic information. Because the information measurement isgenerally the most time-consuming step in the process of determiningattribute values, the elapsed time period over which multiple attributevalues are determined can be considerably shorter than comparable timeperiods associated with methods in which multiple measurement steps areinvolved.

In some embodiments, for example, the elapsed time period during whichthe vibrational spectroscopic information is obtained and one or moreattribute values are determined from the vibrational spectroscopicinformation using chemometric models is 30 seconds or less (e.g., 25seconds or less, 20 seconds or less, 15 seconds or less, 10 seconds orless, 5 seconds or less, 2 seconds or less), starting from a time atwhich incident radiation 130 is incident on the surface of the ATRinterface that contacts fluid 122.

If all attribute values of interest have been determined in step 808,control passes to step 810. In step 810, controller 120 adjusts one ormore process parameters of the biomanufacturing system in response tothe measured attribute values of the process fluid. In general, a widevariety of adjustments can be performed based on the nature of theattribute values that are determined, and the implications of thoseattribute values with respect to the biomanufacturing process. Certainexamples of measured attributes and corresponding adjustments to thebiomanufacturing system are discussed in a subsequent section.

Next, in decision step 812, if determination of attribute values for theprocess fluid is to continue (e.g., for continuous process monitoringand control), control returns to step 804 and new vibrationalspectroscopic information for the process fluid is obtained after anappropriate time interval. Alternatively, if determination of attributevalues is not to continue, control passes to step 814, where the processof flow chart 800 ends.

In general, the process shown in flow chart 800 can be used to determineany number of attribute values from the same set of vibrationalspectroscopic information. For example, in some embodiments, 1 or moreattribute values (e.g., 2 or more attribute values, 3 or more attributevalues, 4 or more attribute values, 5 or more attribute values, 6 ormore attribute values, 8 or more attribute values, 10 or more attributevalues, 12 or more attribute values, or even more attribute values) canbe determined from the same vibrational spectroscopic information usingdifferent chemometric models.

Typically, the set of independent variables that is associated with agiven chemometrics model is different from the sets of independentvariables associated with other chemometrics models used to calculatedifferent attribute values from the same set of vibrationalspectroscopic information. For each chemometrics model, the set ofindependent variables associated with the model can include two or more(e.g., three or more, four or more, five or more, six or more, eight ormore, 10 or more, 12 or more, 15 or more, or even more) principalvibrational components.

However, because the sets of independent variables are generallydetermined independently for each chemometrics model, the sets ofprincipal vibrational components associated with different chemometricsmodels may have no members in common. Alternatively, in someembodiments, set of principal vibrational components associated withdifferent chemometrics models may have 1 or more (e.g., 2 or more, 3 ormore, 4 or more, 5 or more) members in common.

Chemometric Models for Process Fluid and Component Attributes

In general, chemometrics models can be constructed to determine a widevariety of different quality attributes associated with biomanufacturingprocesses. These quality attributes typically fall into one severalcategories including, but not limited to: product quality attributes,which are related to the purity, integrity, yield, morphology, and otherattributes of products of the biomanufacturing processes;product-related impurities, which are related to the nature of differentsynthesis by-products present in process fluids that are produced frombiomanufacturing processes; and process-related impurities, which arerelated to by-products and other undesirable species that result fromprocess conditions within the system.

Chemometrics models can generally be constructed for application tobiomanufacturing of a large number of different species. Some of thequality attributes associated with different categories of species aredifferent, and some are similar. For example, in some embodiments, themethods disclosed herein can be applied to the biomanufacturing ofprotein therapeutic substances such as antibodies, peptides, enzymes,and other species with amino acid chains. For such substances,chemometric models can be constructed to predict values of qualityattributes that include: (a) product quality attributes includingconcentration, aggregates, charge variant distribution, purity, glycanprofile, identity, and integrity; (b) product-related impurities such asprotein fragments; and (c) process-related impurities such as host cellproteins, residual host cell DNA, residual column ligands (e.g., ProteinA), and other impurities such as buffer components, surfactants, processadditives such as insulin, poloxamers, detergents, Polysorbate 80, andother compounds from bioreactors and chromatography/separation columns.

In certain embodiments, the methods disclosed herein can be applied tothe biomanufacturing of nucleic acid drug substances, includingDNA-based species such as DNA, plasmids, oligonucleotides, aptamers, andDNAzymes (e.g., DNase), and RNA-based species such as RNA aptamers, RNAdecoys, microRNAs, and small interfering RNAs. For such substances,chemometric models can be constructed to predict values of qualityattributes that include: (a) product quality attributes includingconcentration, identity, integrity, and aggregates; (b) product-relatedimpurities such as nucleic acid fragments and nucleic acid variants; and(c) process-related impurities such as residual column ligands, andother impurities such as buffer components, surfactants, processadditives such as insulin, poloxamers, detergents, Polysorbate 80), andother compounds from bioreactors and chromatography/separation columns.

In some embodiments, the methods disclosed herein can be applied to thebiomanufacturing of gene therapy drug substances. For such substances,chemometric models can be constructed to predict values of qualityattributes that include: (a) product quality attributes such asconcentration, aggregates, identity, and integrity; (b) product-relatedimpurities such as empty capsids, fragments, and vector impurities; and(c) process-related impurities such as host cell proteins, host cellDNA, residual helper virus, residual helper viral proteins, residualhelper viral DNA, and other impurities such as buffer components,surfactants, process additives such as insulin, poloxamers, detergents,Polysorbate 80), and other compounds from bioreactors andchromatography/separation columns.

Values of any of the foregoing quality attributes can be predictivelygenerated by a suitable chemometrics model. Thus, attribute A inEquations (2) and (3) can represent any of the above quality attributes.

Further, it should be noted that chemometrics models can be constructedto predictively generate values of combinations of any of the abovequality attributes from a single set of measured spectroscopicinformation (e.g., an infrared vibrational spectrum). In general, valuesof combinations of any two or more (e.g., three or more, four or more,five or more, six or more, seven or more, eight or more, ten or more, oreven more) of the above quality attributes can be generated from thesame spectroscopic information by suitably constructed chemometricsmodels.

In the following discussion, specific examples of quality attributesassociated with the biomanufacturing of a protein-based therapeuticsubstance are provided. Spectroscopic measurements are performed onprocess fluid 122 (and components thereof). The values of the qualityattributes determined from the spectroscopic information measured can beused for a variety of purposes, including product quality assessment,biomanufacturing process adjustment, and process termination. Aspects ofthe use of values of quality attributes for biomanufacturing processadjustment by controller 120 will be discussed later.

(i) Antibody Concentration Value

In certain embodiments, such as when the desired product of abiomanufacturing process is an antibody-based pharmaceutical product,the antibody concentration value of a process fluid at a certainlocation within a biomanufacturing system can be related to the overallyield of the desired product. Accordingly, in some embodiments, themethods disclosed herein include obtaining and applying a chemometricsmodel to determine an antibody concentration value for a process fluid.

The set of independent vibrational frequencies that are used in thechemometrics model for the antibody concentration value can includefrequencies within specific frequency ranges. By including frequencieswithin these specific ranges, predictive errors associated withdetermining antibody concentration values from vibrational spectroscopicinformation can be reduced.

For example, in some embodiments, the set of principal vibrationalfrequencies or components used in the chemometrics model for theantibody concentration value can include vibrational spectroscopicinformation in at least one of a wave number range from 1100 cm⁻¹ to1595 cm⁻¹, and a wave number range from 1600 cm⁻¹ to 1700 cm⁻¹. Incertain embodiments, the set of principal vibrational frequencies orcomponents includes at least one component that corresponds tovibrational spectroscopic information in a wave number range from 1100cm⁻¹ to 1595 cm⁻¹, and at least one component that corresponds tovibrational spectroscopic information in a wave number range from 1600cm⁻¹ to 1700 cm⁻¹.

(ii) Extent of Protein Aggregation

In certain embodiments, such as when the desired product of abiomanufacturing process is protein-based, the extent of proteinaggregation in a process fluid can provide important information aboutprotein interaction and product yield. Thus, in some embodiments, themethods disclosed herein include obtaining and applying a chemometricsmodel to determine an extent of protein aggregation in a process fluid.

The set of independent vibrational frequencies that are used in thechemometrics model for the extent of protein aggregation can includefrequencies within specific frequency ranges. For example, in someembodiments, each of the principal vibrational frequencies or componentsused in the chemometrics model for the extent of protein aggregationcorresponds to vibrational spectral information in at least one of a thewave number ranges from 1393 cm⁻¹ to 1554 cm⁻¹, a range from 1600 cm⁻¹to 1635 cm⁻¹, and a range from 844 cm⁻¹ to 1180 cm⁻¹.

In certain embodiments, at least one of the set of principal vibrationalfrequencies or components used in the chemometrics model for the extentof protein aggregation corresponds to vibrational spectral informationin a frequency range from 1393 cm⁻¹ to 1554 cm⁻¹, at least one of theprincipal vibrational frequencies or components corresponds tovibrational spectral information in a frequency range from 1600 cm⁻¹ to1635 cm⁻¹, and at least one of the principal vibrational frequencies orcomponents corresponds to vibrational spectral information in afrequency range from 844 cm⁻¹ to 1180 cm⁻¹.

(iii) Host Cell Protein Quantity

In certain embodiments, the host cell protein quantity for a processfluid in a biomanufacturing system can be used by controller 120 toadjust manufacturing process parameters to improve product yields reduceby-product formation. Thus, the methods disclosed herein can includeobtaining and applying a chemometrics model to determine a host cellprotein quantity in a process fluid.

The set of independent vibrational frequencies that are used in thechemometrics model for the host cell protein quantity can includefrequencies within specific frequency ranges. For example, in certainembodiments, each of the principal vibrational frequencies or componentsused in the chemometrics model for the host cell protein quantity cancorrespond to vibrational spectral information in at least one of awavenumber range from 1500 cm⁻¹ to 1600 cm⁻¹, a wavenumber range from1600 cm⁻¹ to 1680 cm⁻¹, a wavenumber range from 1414 cm⁻¹ to 1489 cm⁻¹,and a wavenumber range from 1174 cm⁻¹ to 1286 cm⁻¹.

In some embodiments, at least one of the principal vibrationalfrequencies or components used in the chemometrics model for the hostcell protein quantity corresponds to vibrational spectral information ina wavenumber range from 1500 cm⁻¹ to 1600 cm⁻¹, at least one of theprincipal vibrational frequencies or components corresponds tovibrational spectral information in a wavenumber range from 1600 cm⁻¹ to1680 cm⁻¹, at least one of the principal vibrational frequencies orcomponents corresponds to vibrational spectral information in awavenumber range from 1414 cm⁻¹ to 1489 cm⁻¹, and at least one of theprincipal vibrational frequencies or components corresponds tovibrational spectral information in a wavenumber range from 1174 cm⁻¹ to1286 cm⁻¹.

(iv) Charge Variant Distribution

In certain embodiments, the charge variant distribution for a processfluid in a biomanufacturing system can be used by controller 120 toadjust manufacturing process parameters. Accordingly, the methodsdisclosed herein can include obtaining and applying a chemometrics modelto determine a charge variant distribution in a process fluid.

It has been determined that certain frequency ranges of principalvibrational frequencies or components for such chemometric models canyield chemometric models that more accurately predict values of thecharge variant distribution. In some embodiments, for example, each ofthe principal vibrational frequencies or components used in thechemometrics model for the charge variant distribution can correspond tovibrational spectroscopic information in at least one of a wavenumberrange from 1118 cm⁻¹ to 1500 cm⁻¹, a wavenumber range from 1120 cm⁻¹ to1470 cm⁻¹, and a wavenumber range from 1187 cm⁻¹ to 1839 cm⁻¹.

In certain embodiments, at least one of the principal vibrationalfrequencies or components used in the chemometrics model for the chargevariant distribution corresponds to vibrational spectroscopicinformation in a wavenumber range from 1118 cm⁻¹ to 1500 cm⁻¹, at leastone of the principal vibrational frequencies or components correspondsto vibrational spectral information in a wavenumber range from 1120 cm⁻¹to 1470 cm⁻¹, and at least one of the principal vibrational frequenciesor components corresponds to vibrational spectral information in awavenumber range from 1187 cm⁻¹ to 1839 cm⁻¹.

Alternatively, in some embodiments, the principal vibrationalfrequencies or components used in the chemometrics model for the chargevariant distribution include a first group of at least three frequenciesor components that correspond to vibrational spectral information in awavenumber range from 1118 cm⁻¹ to 1500 cm⁻¹, a second group of at leastthree frequencies or components that correspond to vibrational spectralinformation in a wavenumber range from 1120 cm⁻¹ to 1470 cm⁻¹, and athird group of at least three frequencies or components that correspondto vibrational spectral information in a wavenumber range from 1187 cm⁻¹to 1839 cm⁻¹. As discussed previously, among the principal vibrationalfrequencies or components, at least one or more may be common to atleast two of the groups, or alternatively, none may be common to any twoor more of the groups.

Chemometrics Models for Bioreactor-Based Analytes

Integrated continuous biomanufacturing systems typically implementcontinuous capture and post-capture processing (i.e., purification,polishing, and filtration) of drug substances and other reactor-derivedproducts. In both continuous operation systems and more conventionalbatch-based bioreactor systems, methods for assessing bioreactorconditions and adjusting various process parameters are important toensure high yields. Moreover, for large-scale manufacturing operations,and under relatively sensitive internal bioreactor conditions, it isdesirable that methods for monitoring process parameters be highlyautomated, robust, and provide information that can be used toautomatically adjust bioreactor conditions.

The specific cell lines and chemical media used under productionconditions have been shown to significantly affect volumetric andspecific productivity of a bioreactor. In particular, under suitablechoice of cell line and bioreactor media, viable cell density (VCD) andvolumetric productivity have been sustained for relatively long periodsof time. FIGS. 18A-18D are graphs showing viable cell density (FIG.18A), cell viability (FIG. 18B), harvest titer (FIG. 18C), and specificproductivity (FIG. 18D) over a period of 45 days for a perfusionbioreactor. These data indicate that under the tested conditions, thereactor can be operated at a 1.5 RV/day perfusion rate with 100×10⁶viable cells/mL, with the viable cell density, cell viability, harvesttiter, and specific productivity remaining relatively stable after theinitial reactor conditions reach equilibrium.

Different VCD, pH conditions, and perfusion rates can adjust conditionswithin a bioreactor. Specifically, changes in any one or more of theseoperating parameters can change levels of various intermediates andmedium components such as lactate, ammonium ions, glucose, andglutamine. Due to the importance of these intermediates in regulatingproduction rates and affecting cell viability, values of many suchquantities may be measured and adjusted relatively frequently to ensurethat the bioreactor operates within a range of conditions that ensurestability and relatively high productivity. FIGS. 19A-19D are graphsshowing how glucose concentrations (FIG. 19A), glutamine concentrations(FIG. 19B), lactate concentrations (FIG. 19C), and ammonium ionconcentrations (FIG. 19D) vary under different cell density, pH, andperfusion conditions for a particular bioreactor producing a monoclonalantibody product.

Due to the effect of these and other cell medium components and processintermediates on cell viability and productivity, measurement of valuesof these and other components provides important information aboutinternal bioreactor processes, and can further provide data used toperform automated adjustment of bioreactor conditions that have deviatedtoo far from specific target conditions. FIGS. 20A-20D are graphsshowing daily at-line measurements of glucose concentration (FIG. 20A),harvest titer (FIG. 20B), lactate concentration (FIG. 20C), and ammoniumion concentration (FIG. 20D). The data in FIGS. 20A-20D were obtained indaily manual measurements, in which the bioreactor medium was sampledand separate analyses were conducted for each of the quantities in FIGS.20A-20D.

Unfortunately, manual measurement of values of each of the foregoingquantities can be quite time-consuming, which limits the rate at whichmeasurements can be made. The slower the rate at which measurements aremade, the longer the delay before corrective adjustment of bioreactorconditions can occur. As such, product yields may be adversely affectedif cell viability departs too significantly from ideal conditions.Further, frequency manual collection and processing of samples tomeasure values of these bioreactor medium components results inrelatively high use of consumables associated with the sampling methodsemployed. Over time, the attendant cost of consumables can besignificant.

The in-line infrared measurement techniques and chemometric methodsdiscussed previously can be directly applied to the measurement ofbioreactor medium-based components such as glucose, glutamine, lactate,and ammonium ions. The speed at which infrared spectral information canbe obtained, and the non-invasive manner in which it can be obtained,make such measurements highly advantageous compared with manual samplingand analysis methods. Furthermore, chemometric methods with validatedmodels allow values of multiple quantities to be extracted from a singleset of infrared spectral information. As such, concentrations ofglucose, glutamine, lactate, and ammonium ions (and other mediumcomponents) can each be derived from a single measured infrared spectrumanalyzed with suitable chemometric models, which significantly reducesthe number of spectral measurements that are performed, and in turnincreases the rate at which bioreactor conditions can be adjusted inresponse to the measured values.

Integration and Adjustment of Biomanufacturing Systems

The measurement systems disclosed herein can be integrated withbio-manufacturing systems to provide feedback control to variouscomponents and steps in synthesis and purification processes for avariety of biological products. The measurement systems are typicallyimplemented in-line between components of the manufacturing systems, sothat flowing or stationary solutions can be analyzed in real time withno sampling or diversion. The measurement systems can also be used moreconventionally with samples extracted from reaction vessels, holdingtanks, or chromatography columns prior to performing infraredspectroscopic measurements.

Integrated and fully continuous processes for manufacturing therapeuticprotein drugs and other substances can include, e.g., providing a liquidculture medium containing a recombinant therapeutic protein that issubstantially free of cells, then feeding the liquid culture medium intoa first multi-column chromatography system (MCCS1). The next stepinvolves capturing the recombinant therapeutic protein in the liquidculture medium using the MCCS1, and then continuously feeding the eluateof the MCCS1 containing the recombinant therapeutic protein into asecond multi-column chromatography system (MCCS2), and purifying andpolishing the protein using the MCCS2. The resulting eluate from theMCCS2 is considered a therapeutic protein drug substance. The processesare integrated and can run continuously from the liquid culture mediumto the eluate from the MCCS2 that is the therapeutic protein drugsubstance.

Bio-manufacturing systems are typically used to perform the aboveprocesses. For example, such systems can include a MCCS1 that includesan inlet and a MCCS2 that includes an outlet. In these systems, thefirst and second MCCSs are in fluid communication with each other. Thesystems are also configured such that fluid can be passed into theinlet, through the first and second MCCSs, and exit the manufacturingsystem through the outlet.

Such systems can provide for continuous and time-efficient production ofa therapeutic drug substance from a liquid culture medium. For example,the elapsed time between feeding a fluid (e.g., a liquid culture medium)containing a therapeutic protein into the first MCCS and eluting atherapeutic protein drug substance (containing the therapeutic protein)from the outlet of the second MCCS can be, e.g., between about 4 hoursand about 48 hours.

FIG. 10 is a schematic diagram showing an example of a bio-manufacturingsystem. System 1 includes a first MCCS, i.e., a four-column PeriodicCounter-Current Chromatography System (PCCS) 2, where three of the fourcolumns 3, 4, and 5 in four-column PCCS 2 perform the unit operation ofcapturing the recombinant therapeutic protein from a fluid containingthe recombinant therapeutic protein (e.g., liquid culture medium that issubstantially free of mammalian cells), and one of the columns 6 in PCCS2 performs the unit operation of inactivating viruses present in theeluate from columns 3, 4, and 5 in PCCS 2 containing the recombinanttherapeutic protein. Columns 3, 4, and 5 can contain a resin thatutilizes a Protein A-binding capture mechanism. Column 6 is capable ofholding a fluid at a pH of about 3.75 for about 1 hour. PCCS 1 also hasan inlet 7. Inlet 7 can be, e.g., an orifice that accepts entry of afluid into PCCS 1.

System 1 also includes a second MCCS that is a PCCS 8 that includesthree chromatography columns 9, 10, and 11 and one chromatographicmembrane 12. Columns 9, 10, and 11 in PCCS 8 can contain a cationicexchange resin. Chromatographic membrane 12 in PCCS 8 can contain acationic exchange resin. PCCS 8 also has a fluid conduit 13 disposedbetween columns 9, 10, and 11 in PCCS 8 and chromatographic membrane 12in PCCS 8. PCCS 8 also has an in-line buffer adjustment reservoir 14that is in fluid communication with fluid conduit 13, and is configuredsuch that buffer contained within in-line buffer adjustment reservoir 14is introduced into the fluid present in fluid conduit 13. PCCS 8 alsoincludes an outlet 15. Outlet 15 can be, e.g., an orifice that allowsexit of the fluid from PCCS 8.

System 1 can further include a fluid conduit 16 disposed between PCCS 2and PCCS 8. System 1 can also include an in-line buffer adjustmentreservoir 17 in fluid communication with fluid conduit 16 configuredsuch that the buffer contained within in-line buffer adjustmentreservoir 17 can be introduced into the fluid present in fluid conduit16. System 1 can also include a filter 18 disposed in fluid conduit 16to filter the fluid present in fluid conduit 16. System 1 can alsoinclude a break tank 19 disposed in fluid conduit 16 and configured tohold any fluid in fluid conduit 16 that cannot be readily fed into PCCS8.

System 1 can further include a pump system 20 that is in fluidcommunication with inlet 7. Pump system 20 can include a pump 21 forpushing fluid into inlet 7. System 1 can also include a fluid conduit 22disposed between pump 21 and inlet 7. System 1 can also include a filter23 disposed in fluid conduit 22 to filter the fluid (e.g., liquidculture medium) present in fluid conduit 22. System 1 can also include abreak tank 24 disposed in fluid conduit 22 configured such that breaktank 24 is in fluid communication with fluid conduit 22 and is capableof storing any fluid present in fluid conduit 22 that is not able toenter inlet 7.

System 1 can also include a bioreactor 25 and a fluid conduit 26disposed between bioreactor 25 and pump 21. A filtration system 27 maybe disposed in fluid conduit 26 to filter (e.g., remove cells from) aliquid culture medium present in fluid conduit 26.

The first MCCS (PCCS 2) includes an inlet through which fluid (e.g., aliquid culture medium that is substantially free of cells) can be passedinto the first MCCS. The inlet can be any structure known in the art forsuch purposes. It can include, e.g., a threading, ribbing, or a sealthat allows for a fluid conduit to be inserted, such that afterinsertion of the fluid conduit into the inlet, fluid will enter thefirst MCCS through the inlet without significant seepage of fluid out ofthe inlet.

The first MCCS includes at least two chromatography columns, at leasttwo chromatographic membranes, or at least one chromatography column andat least one chromatographic membrane, and an inlet. For example, thefirst MCCS can include a total of four chromatography columns, or threechromatography columns and one chromatographic membrane, or any of theother exemplary MCCSs described herein, or have one or more of any ofthe exemplary features of a MCCS (in any combination) described herein.

The chromatography column(s) and/or the chromatographic membrane(s)present in the first MCCS can contain one or more of a variety ofresins. For example, the resin contained in one or more of thechromatography column(s) and/or chromatographic membrane(s) present inthe first MCCS can be a resin that utilizes a capture mechanism (e.g.,Protein A-binding capture mechanism, protein G-binding capturemechanism, antibody- or antibody fragment-binding capture mechanism,substrate-binding capture mechanism, cofactor-binding capture mechanism,an aptamer-binding capture mechanism, and/or a tag-binding capturemechanism). The resin contained in one or more of the chromatographycolumn(s) and/or chromatographic membrane(s) of the first MCCS can be acation exchange resin, an anion exchange resin, a molecular sieve resin,or a hydrophobic interaction resin, or any combination thereof.Additional examples of resins that can be used to purify a recombinanttherapeutic protein are known in the art, and can be contained in one ormore of the chromatography column(s) and/or chromatographic membrane(s)present in the first MCCS. The chromatography column(s) and/orchromatography membranes present in the first MCCS can contain the sameand/or different resins (e.g., any of the resins described herein orknown in the art for use in recombinant protein purification).

The two or more chromatography column(s) and/or chromatographic resin(s)present in the first MCCS can perform one or more unit operations (e.g.,capturing a recombinant therapeutic protein, purifying a recombinanttherapeutic protein, polishing a recombinant therapeutic protein,inactivating viruses, adjusting the ionic concentration and/or pH of afluid containing the recombinant therapeutic protein, or filtering afluid containing a recombinant therapeutic protein). In non-limitingexamples, the first MCCS can perform the unit operations of capturing arecombinant therapeutic protein from a fluid (e.g., a liquid culturemedium) and inactivating viruses present in the fluid containing therecombinant therapeutic protein. The first MCCS can perform anycombination of two of more unit operations described herein or known inthe art.

The chromatography column(s) and/or chromatographic membrane(s) presentin the first MCCS can be connected or moved with respect to each otherby a switching mechanism (e.g., a column-switching mechanism). The firstMCCS can also include one or more (e.g., two, three, four, or five)pumps (e.g., automated, e.g., automated peristaltic pumps). Thecolumn-switching events can be triggered by the detection of a level ofrecombinant therapeutic protein in the fluid passing through the firstMCCS (e.g., the input into and/or eluate from one or more of thechromatography column(s) and/or chromatographic membranes in the firstMCCS), a specific volume of liquid (e.g., buffer), or specific timeelapsed. Column switching generally means a mechanism by which at leasttwo different chromatography columns and/or chromatographic membranes inan MCCS (e.g., two or more different chromatography columns and/orchromatographic membranes present in an MCCS (e.g., the first or secondMCCS)) are allowed to pass through a different step (e.g.,equilibration, loading, eluting, or washing) at substantially the sametime during at least part of the process.

PCCS 2 that is the first MCCS can include four chromatography columns,where the first three columns perform the unit operation of capturing arecombinant therapeutic protein from a fluid (e.g., a liquid culturemedium), and the fourth column of the PCCS performs the unit operationof inactivating viruses in the fluid containing the recombinanttherapeutic protein. A PCCS that is the first MCCS can utilize acolumn-switching mechanism. The PCC system can utilize a modified AKTAsystem (GE Healthcare, Piscataway, N.J.) capable of running up to, e.g.,four, five, six, seven, or eight columns, or more.

Column switching events can be triggered by detection of a concentrationof a particular protein or other substance in a fluid eluting from oneof the columns of PCCS 2 or PCCS 8, flowing through a filter in theMCCS, contained in a break tank of the MCCS, or flowing through aconduit in the MCCS (e.g., between MCCS 1 and MCCS 2). The measurementsystems disclosed herein can be used to measure concentrations of suchproteins, and to transmit the concentration information to a controllerin system 1 that initiates events such as column switching, filtering,and fluid transport in system 1.

The first MCCS can be equipped with: one or more (e.g., two, three,four, five, six, seven, eight, nine, or ten) measurement systemsconfigured to obtain infrared spectroscopic information for processfluids (e.g., system 100), one or more (e.g., two, three, four, five,six, seven, eight, nine, or ten) valves, one or more (e.g., two, three,four, five, six, seven, eight, nine, or ten) pH meters, and/or one ormore (e.g., two, three, four, five, six, seven, eight, nine, or ten)conductivity meters. The first MCCS can also be equipped with acontroller executing an operating system that utilizes software (e.g.,Unicorn-based software, GE Healthcare, Piscataway, N.J., or othersoftware implementing similar functionality) for determining when acolumn-switching should occur (e.g., based upon concentrationinformation derived from infrared spectroscopic measurements, volume ofliquid, or elapsed time) and affecting (triggering) the column-switchingevents. The measurement systems can be placed optionally at the inlet ofone or more (e.g., two, three, four, five, six, seven, eight, nine, orten) of the chromatography column(s) and/or chromatographic membrane(s)in the first MCCS, and/or at the outlet of one or more of thechromatography column(s) and/or chromatography membrane(s) in the firstMCCS.

The first MCCS can further include one or more (e.g., two, three, four,five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one,twenty-two, twenty-three, or twenty-four) in-line buffer adjustmentreservoir(s) and/or a buffer reservoir(s). In other examples, the firstMCCS can include one or more (e.g., two, three, four, five, or six)break tanks that can hold fluid that cannot readily pass into one ormore of the chromatography columns and/or chromatographic membranes inthe first MCCS. The systems described herein can contain one or morebreak tanks (e.g., a break tank described herein) in the first and/orsecond MCCS. Other examples of the systems described herein do notinclude a break tank in the first MCCS or the second MCCS, or do notinclude a break tank in the entire system. Other examples of the systemsinclude a maximum of one, two, three, four, or five break tank(s) in theentire system.

In some embodiments, the first MCCS can include a viral inactivationdevice. For example, referring to FIG. 10, in certain embodiments thefirst MCCS includes viral inactivation device 6 (i.e., in place ofcolumn 6 described above). Viral inactivation device 6 is configured toinactivate viruses and viral vectors used in biomanufacturing processes.In some embodiments, for example, viral inactivation device 6 includes amixing vessel. Alternatively, in certain embodiments for example, device6 includes a plug flow inactivation system. Each of these examples ofviral inactivation devices helps to eliminate active viruses and viralvectors from process fluids in the first MCCS.

The second MCCS includes at least two chromatography columns, at leasttwo chromatographic membranes, or at least one chromatography column(s)and at least one chromatographic membrane(s), and an outlet. Forexample, the second MCCS can include a total of four chromatographycolumns, three chromatography columns and one chromatographic membrane,or any of the other exemplary MCCSs described herein, or can have one ormore of any of the exemplary features of an MCCS (in any combination)described herein. The chromatography column(s) and/or thechromatographic membrane(s) present in the second MCCS can have one ormore of: any of the shapes, sizes, volumes (bed volumes), and/or unitoperations described herein. The resin contained in one or more of thechromatography column(s) and/or chromatographic membrane(s) present inthe second MCCS can be a resin that utilizes a capture mechanism (e.g.,Protein A-binding capture mechanism, Protein G-binding capturemechanism, antibody- or antibody fragment-binding capture mechanism,substrate-binding capture mechanism, cofactor-binding capture mechanism,tag-binding capture mechanism, and/or aptamer-binding capturemechanism). Useful resins include, e.g., a cation exchange resin, ananion exchange resin, a molecular sieve resin, and a hydrophobicinteraction resin. The chromatography column(s) and/or chromatographymembranes present in the second MCCS can contain the same and/ordifferent resins (e.g., any of the resins described herein or known inthe art for use in recombinant protein purification).

The chromatography column(s) and/or chromatographic membrane(s) presentin the second MCCS can perform one or more unit operations (e.g., any ofthe unit operations described herein or any combination of the unitoperations described herein). In non-limiting examples, the second MCCScan perform the unit operations of purifying a recombinant therapeuticprotein from a fluid and polishing the recombinant therapeutic proteinpresent in the fluid containing the recombinant therapeutic protein. Inother non-limiting examples, the second MCCS can perform the unitoperations of purifying a recombinant therapeutic protein present in afluid, polishing a recombinant therapeutic protein present in a fluid,and filtering a fluid containing a recombinant therapeutic protein. Inanother example, the second MCCS can perform the unit operations ofpurifying a recombinant therapeutic protein present in a fluid,polishing a recombinant therapeutic protein present in a fluid,filtering a fluid containing a recombinant therapeutic protein, andadjusting the ionic concentration and/or pH of a fluid containing arecombinant therapeutic protein. The second MCCS can perform anycombination of two of more unit operations described herein or known inthe art.

The second MCCS can also include one or more (e.g., two, three, four, orfive) pumps (e.g., automated, e.g., automated peristaltic pumps).

The chromatography column(s) and/or chromatographic membrane(s) presentin the second MCCS can be connected or moved with respect to each otherby a switching mechanism (e.g., a column-switching mechanism). Thecolumn-switching events can be triggered by the detection of a level ofrecombinant therapeutic protein or other substance via infraredspectroscopic measurements and analysis thereof using chemometricmodels, as discussed above, to determine the level of recombinanttherapeutic protein in the fluid passing through the second MCCS (e.g.,the input into and/or eluate from one or more of the chromatographycolumn(s) and/or chromatographic membranes in the second MCCS), aspecific volume of liquid (e.g., buffer), or specific time elapsed.

The PCCS 8 that forms the second MCCS can contain three columns thatperform the unit operation of purifying a recombinant therapeuticprotein from a fluid, and a chromatographic membrane that performs theunit operation of polishing a recombinant therapeutic protein present ina fluid. For example, the three columns that perform the unit operationof purifying a recombinant therapeutic protein from a fluid can contain,e.g., a cationic exchange resin, and the chromatographic membrane thatperforms the unit operation of polishing can contain a cationic exchangeresin. A PCCS that is the second MCCS can utilize a column-switchingmechanism. For example, the PCCS can utilize a modified AKTA system (GEHealthcare, Piscataway, N.J.) capable of running up to, e.g., four,five, six, seven, or eight columns, or more.

Similar to the first MCCS, the second MCCS can also be equipped with:one or more (e.g., two, three, four, five, six, seven, eight, nine, orten) infrared spectroscopic measurement systems, one or more (e.g., two,three, four, five, six, seven, eight, nine, or ten) valves, one or more(e.g., two, three, four, five, six, seven, eight, nine, or ten) pHmeters, and/or one or more (e.g., two, three, four, five, six, seven,eight, nine, or ten) conductivity meters. The one or more measurementsystems transmit concentration information for the protein or othersubstance in the fluid that is measured to a controller that uses theconcentration information to determine whether to trigger a columnswitching event. The second MCCS can be equipped with an operatingsystem, executed by the controller that receives the concentrationinformation, that utilizes software (e.g., Unicorn-based software, GEHealthcare, Piscataway, N.J.) for determining when a column-switchingevent should occur (e.g., based upon infrared spectroscopicmeasurements, volume of liquid, or elapsed time) and initiating thecolumn-switching events. In the examples where the second MCCS includesone or more infrared spectroscopic measurement systems, the measurementsystems can be placed optionally at the inlet of one or more (e.g., two,three, four, five, six, seven, eight, nine, or ten) of thechromatography column(s) and/or chromatographic membrane(s) in thesecond MCCS, and/or at the outlet of one or more of the chromatographycolumn(s) and/or chromatography membrane(s) in the second MCCS.

The second MCCS can further include one or more (e.g., two, three, four,five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one,twenty-two, twenty-three, or twenty-four) in-line buffer adjustmentreservoir(s) and/or a buffer reservoir(s). In other examples, the secondMCCS can include one or more (e.g., two, three, four, five, or six)break tanks (e.g., any of the break tanks described herein) that canhold fluid that cannot readily pass into one or more of thechromatography columns and/or chromatographic membranes in the secondMCCS.

The second MCCS includes an outlet through which the therapeutic proteindrug substance can exit the system. The outlet can include, e.g., athreading, ribbing, or a seal that allows for a fluid conduit to beinserted or a vial designed to contain or store the therapeutic proteindrug substance. An outlet can contain a surface that can be used to seala sterile vial or other such storage container onto the outlet in orderto allow the recombinant protein drug product to flow directly into thesterile vial or storage container.

One or more infrared spectroscopic measurement systems, as disclosedherein, can also be positioned to measure the concentration of theprotein drug substance (or another substance) flowing out of the outlet.This information can be transmitted to the MCCS controller, which candetermine a purity of the substance based on the information.

The systems described herein can also include a fluid conduit that isdisposed between the first MCCS and the second MCCS. One or moreinfrared spectroscopic measurement systems can be disposed along thefluid conduit to determine information (e.g., concentration information)about fluids held within (e.g., flowing through) the conduit. Thisinformation can be communicated to a MCCS controller which, as discussedabove, can determine whether to initiate a column-switching event basedon the information.

Any of the fluid conduits described herein can be, e.g., a tube that ismade of, e.g., polyethylene, polycarbonate, or plastic. The fluidconduit disposed between the first MCCS and the second MCCS can furtherinclude one of more of the following in any combination: one or morein-line buffer adjustment reservoirs that are in fluid communicationwith the fluid conduit and are positioned such that the buffer storedwithin the in-line buffer adjustment reservoir(s) is added to the fluidpresent in the fluid conduit; a break tank (e.g., any of the breaktank(s) described herein) that is in fluid communication with the fluidconduit and is positioned such that it can hold any excess fluid presentin the fluid conduit that is unable to readily feed into the secondMCCS; and one or more filters that are disposed in the fluid conduitsuch that they are capable of filtering (e.g., removing bacteria) thefluid present in the fluid conduit. Any of the in-line buffer adjustmentreservoirs can contain, e.g., a volume of between about 0.5 L to 50 L ofbuffer (e.g., at a temperature at or below 50° C., 37° C., 25° C., 15°C., or 10° C.).

The systems described herein can optionally include a fluid conduitdisposed between the final chromatography column or chromatographicmembrane in the second MCCS and the outlet. The systems described hereincan further include one or more filters in fluid connection with thefluid conduit disposed between the final chromatography column orchromatographic membrane in the second MCCS and the outlet, such thatthe filter can remove, e.g., precipitated material, particulate matter,or bacteria from the fluid present in the fluid conduit disposed betweenthe final chromatography column or chromatographic membrane in thesecond MCCS and the outlet.

Some examples of the systems provided herein also include a bioreactorthat is in fluid connectivity with the inlet of the first MCCS. Any ofthe exemplary bioreactors described herein or known in the art can beused in the present systems.

Some examples of the systems provided herein also include a pump system.A pump system can include one or more the following: one or more (e.g.,two, three, four, five, six, seven, eight, nine, or ten) pumps (e.g.,any of the pumps described herein or known in the art), one or more(e.g., two, three, four, or five) filters (e.g., any of the filtersdescribed herein or known in the art), one or more (e.g., two, three,four, five, six, seven, eight, nine, or ten) UV detectors, and one ormore (e.g., two, three, four, or five) break tanks (e.g., any of thebreak tanks described herein).

Some examples of the systems provided herein further include a fluidconduit disposed between the pump and the inlet of the first MCCS (e.g.,any of the exemplary fluid conduits described herein or known in theart). In some examples, this particular fluid conduit can include one ormore (e.g., two, three, or four) pumps (e.g., any of the pumps describedherein or known in the art) and/or one or more (e.g., two, three, orfour) break tanks (e.g., any of the exemplary break tanks describedherein), where these pump(s) and/or break tank(s) are in fluidconnection with the fluid present in the fluid conduit.

Some examples of the systems described herein further include a furtherfluid conduit connected to the fluid conduit between the pump and theinlet, where one end of the further fluid conduit is fluidly connectedto a bioreactor and the other end is fluidly connected to the fluidconduit between the pump and the inlet. This further fluid conduit caninclude a filter that is capable of removing cells from the liquidculture medium removed from the bioreactor (e.g., ATF cell retentionsystem).

The foregoing bio-manufacturing systems allow for the continuousproduction of a therapeutic protein drug substance. For example, thesystems provided herein allow for a percentage yield of recombinanttherapeutic protein (from a starting material, e.g., a starting liquidculture medium) of greater than about 70%, greater than about 80%,greater than about 82%, greater than about 84%, greater than about 86%,greater than about 88%, greater than about 90%, greater than about 92%,greater than about 94%, greater than about 96%, or greater than about98%. The systems described herein can also result in a percentage yieldof recombinant therapeutic protein (from a starting material, e.g., astarting liquid culture medium) of between about 80% to about 90%,between about 82% to about 90%, between about 84% to about 90%, betweenabout 84% to about 88%, between about 84% to about 94%, between about82% to about 92%, or between about 85% to about 95%.

The systems described herein can also result in the production of atherapeutic protein drug substance that contains a concentration ofrecombinant therapeutic protein that is greater than about 1.0 mg/mL,e.g., greater than about 15 mg/mL, greater than about 20 mg/mL, greaterthan about 25 mg/mL, greater than about 30 mg/mL, greater than about 35mg/mL, greater than about 40 mg/mL, greater than about 45 mg/mL, greaterthan about 50 mg/mL, greater than about 55 mg/mL, greater than about 60mg/mL, greater than about 65 mg/mL, greater than about 70 mg/mL, greaterthan about 75 mg/mL, greater than about 80 mg/mL, greater than about 85mg/mL, greater than about 90 mg/mL, greater than about 100 mg/mL,greater than about 125 mg/mL, or greater than about 150 mg/mL.

As discussed above, in some embodiments, the first and/or second MCCScan be a Periodic Counter-Current Chromatography System (PCCS). A PCCScan, e.g., include two or more chromatography columns (e.g., threecolumns or four columns) that are switched in order to allow for thecontinuous elution of recombinant therapeutic protein from the two ormore chromatography columns. A PCCS can include two or morechromatography columns, two or more chromatographic membranes, or atleast one chromatographic column and at least one chromatographicmembrane. A column operation generally consists of the load, wash,elute, and regeneration steps. In PCCSs, multiple columns are used torun the same steps discretely and continuously in a cyclic fashion.Since the columns are operated in series, the flow through and wash fromone column is captured by another column. This unique feature of PCCSsallows for loading of the resin close to its static binding capacityinstead of to the dynamic binding capacity, as is typical during batchmode chromatography.

An example of the three column-switching technique used in a PCCScontaining three columns is shown in FIG. 11. A cycle is defined asthree complete column operations resulting in an elution pool from eachof the three columns used in the column-switching technique. Once allthe steps in the cycle are completed, the cycle is re-started. As aresult of the continuous cycling and elution, fluid entering a PCCS isprocessed continuously, while recombinant therapeutic protein elutionfrom each column is discrete and periodic.

To advance from one step to another in a PCCS cycle, such as theexemplary cycle shown in FIG. 11, a column-switching strategy isemployed. The column switching method employs two automated switchingoperations per column in the three-columns in the exemplary PCCS systemshown in FIG. 11, the first of which is related to the initial productbreakthrough, while the second coincides with column saturation. Thedetermination of when the column switching operations should take placeis based on information about recombinant therapeutic proteinconcentrations in the eluate from each chromatography column in thePCCS.

As discussed above, the infrared spectroscopic measurement systemsdisclosed herein can be used to determine concentrations of recombinanttherapeutic proteins in eluate from PCCS columns. The concentrationinformation—which functions as a feedback control for thebio-manufacturing system—is transmitted to the MCCS controller, whichinitiates column switching after determining that a switch is warranted.

As an example, during column loading, the PCC control system candetermine a baseline concentration of a therapeutic protein substanceeluting from the column (which is typically zero concentration) usingthe infrared spectroscopic measurement systems discussed above. Duringactive elution, as the protein substance breaks through, there is anincrease (e.g., above the baseline concentration) in the measuredprotein concentration. The system continues to monitor the increasingprotein concentration, and when the concentration reaches apre-determined threshold value, the flow-through from column 1 isdirected onto column 2 instead of to the waste. Nominally, this occursat a time t₁.

As the feed continues into column 1, column 1 eventually becomes nearlysaturated with the protein product. At this point, the measuredconcentration of protein in the eluate has reached anotherpre-determined value, which occurs at a time t₂. At this point, the MCCScontroller switches the inlet feed to column 2.

The above column-switching strategy allows for the uniform loading ofthe columns irrespective of the feed product concentration and thecapacity. Similar switches of the columns based on the level ofrecombinant protein detected in the eluate from each column can beimplemented. Column switches can also be based on elapsed time or theamount of fluid (e.g., buffer) passed through the one or morechromatography column(s) and/or chromatographic membranes in the firstor second MCCS.

In addition to providing feedback information to control columnswitching events, the measurement systems disclosed herein can alsoprovide feedback information for the adjustment of various otherbio-manufacturing steps and operating parameters. One example of suchadjustments is the controlled adjustment of buffer concentrations atvarious stages of the bio-manufacturing processes.

In general, one or more (e.g., three, four, five, six, seven, eight,nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen,seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two,twenty-three, or twenty-four) different types of buffer can be employedduring the use of the two or more MCCSs in any of the processesdescribed herein. As is known in the art, the one or more types ofbuffer used in the two or more MCCSs used in the processes describedherein will depend on the resin present in the chromatography column(s)and/or the chromatographic membrane(s) of the two or more MCCSs (e.g.,the first and second MCCSs), the recombinant therapeutic protein, andunit operation (e.g., any of the exemplary unit operations describedherein) performed by the specific chromatography column(s) and/orchromatography membranes of the two or more MCCSs. The volume and typeof buffer employed during the use of the two or more MCCSs in any of theprocesses described herein can also be determined by one skilled in theart (e.g., discussed in more detail below). For example, the volume andtype(s) of buffer employed during the use of the two or more MCCSs inany of the processes described herein can be chosen in order to optimizeone or more of the following in the recombinant protein drug product:the overall yield of recombinant therapeutic protein, the activity ofthe recombinant therapeutic protein, the level of purity of therecombinant therapeutic protein, and the removal of biologicalcontaminants from a fluid containing the recombinant therapeutic protein(e.g., absence of active viruses, mycobacteria, yeast, bacteria, ormammalian cells).

The unit operations of adjusting the ionic concentration and/or pH of afluid containing the recombinant therapeutic protein can be performedusing a MCCS (e.g., the first and/or second MCCS) that includes andutilizes a buffer adjustment reservoir (e.g., an in-line bufferadjustment reservoir) that adds a new or additional buffer solution intoa fluid that contains the recombinant therapeutic protein (e.g., betweencolumns within a single MCCS, or after the last column in a penultimateMCCS (e.g., the first MCCS) and before the fluid containing therecombinant therapeutic protein is fed into the first column of the nextMCCS (e.g., the second MCCS). The in-line buffer adjustment reservoircan be any size (e.g., greater than 100 mL) and can contain any bufferedsolution (e.g., a buffered solution that has one or more of: anincreased or decreased pH as compared to the fluid containing therecombinant therapeutic protein, an increased or decreased ionic (e.g.,salt) concentration compared to the fluid containing the recombinanttherapeutic protein, and/or an increased or decreased concentration ofan agent that competes with the recombinant therapeutic protein forbinding to resin present in at least one chromatographic column or atleast one chromatographic membrane in an MCCS (e.g., the first or thesecond MCCS)).

In some embodiments, determination by the MCCS controller of the amountof buffer solution to add to process fluid is based on concentrationinformation about a component of the process fluid derived from infraredspectroscopic measurements performed as discussed previously. Forexample, the solute for purposes of such measurements can be a buffersolution component or a component of the process fluid for which theconcentration is related to the fluid buffer composition, the pH of theprocess fluid, and/or the ionic strength of the process fluid.Measurement of the concentration information for the component isprovided as feedback information to the MCCS controller, which uses thefeedback information to determine when and what quantity of one or morebuffer solutions to discharge into the process fluid. Infraredspectroscopic measurement systems can generally be positioned at anylocation in the bio-manufacturing system for purposes of measuringprocess fluids to provide buffer-related feedback information to theMCCS controller.

In certain embodiments, antibody concentration information for a processfluid can be used to control a rate at which cell cultures areintroduced into a bioreactor. In particular, by determining the antibodyconcentration value in a process fluid harvested from the bioreactor,the MCCS controller can adjust the bleed rate of the cell culture intothe bioreactor. Adjustment in this manner allows control of thevolumetric productivity derived from cell density and specificproductivity of the bioreactor. For a fixed perfusion rate, suchadjustments permit control of the antibody concentration in the processfluid such that MCCS1 would receive an approximately constant amount ofproduct per unit time. In other words, adjustments of this nature can beused to ensure that the rate of product generation within the bioreactorremains approximately constant over a particular time period.

In some embodiments, determination of certain quality attributesassociated with process fluids can be used by the MCCS controller todetermine whether a biomanufacturing system is operating within anacceptable range of parameters, or whether during operation, the systemis outside one or more acceptable parameter ranges.

For each of one or more quality attributes, a range of acceptable valuescan be established through calibration procedures. These rangeseffectively establish operating conditions for the system under whichbiological products are generated at acceptable rates and levels ofpurity, while the yields of by-products and other undesirable speciesare at acceptably low levels. When the system operates outside of one ormore of the ranges, product yields and/or purity may be reduced,rates/quantities of production of undesirable species may be increased,reagent consumption rates may be increased, and/or other undesirableeffects or conditions may result.

Quality attributes determined for process fluids at one or morelocations within the system can be used to ensure that the systemoperates within acceptable ranges of these operating parameters. If thedetermined values of one or more of the quality attributes fall outsidethe established acceptable ranges, the MCCS controller identifies that apotential fault condition exists.

To address a fault condition, the MCCS controller (or another systemcontroller connected to the MCCS controller) can adjust any of theoperating parameters of the biomanufacturing system to modify itsoperation, thereby also adjusting values of the quality attributes suchthat they fall within acceptable ranges. Corrective actions of thisnature ensure that, based on feedback provided by the determined valuesof the quality attributes, the system can be actively maintained withinan established set or range of operating conditions.

In certain embodiments, if the MCCS controller (or another systemcontroller connected to the MCCS controller) determines that the systemis too far out of range from its acceptable range of operatingconditions such that returning the system to an acceptable range ofconditions would be difficult or even impossible, or would result inother undesirable consequences, the controller can transmit controlsignals to the bioreactor to discontinue production and discharge itscontents to waste. In such a case, effective corrective action isimpractical or impossible—the production process has deviated too farfrom the acceptable range of operating conditions for the system. Bysimply discharging the contents of the bioreactor, the system can saveconsiderable time by restarting the production process, rather thanattempting to adjust an ongoing production process that may havedeviated irretrievably from an acceptable range of conditions.

Further, feedback can be provided to the MCCS controller (or to anothersystem controller) based on measured values of one or more bioreactormedium components (such as glucose concentration, glutamineconcentration, lactate concentration, and ammonium ion concentration),which can then be used to adjust reactor conditions to ensure that cellviability, product yields, and other performance metrics are maintainedwithin target ranges. Any one or more process parameters can be adjustedby the controller based on values of the bioreactor medium components ina manner similar to adjustments made based on product quality attributesand values of other measured quantities.

EXAMPLES

The following examples are provided to further illustrate variousaspects of the foregoing disclosure, but are not intended to otherwiselimit any features of the claims, or limit any aspects of theembodiments unless expressly stated.

To evaluate the methods and systems disclosed herein, FTIR spectroscopyusing the ATR geometry and partial least-squares multivariate dataanalysis were used to develop chemometric models for rapid and accuratedetermination of multiple process physical and chemical attributes usinga single set (Multi Attribute Product Quality (MAP-Q) of vibrationalspectroscopic information. Protein A purified samples from multipleharvest days of antibody drug candidate-X were analyzed for theirconcentration, aggregation, charge variant distribution, and host cellprotein (HCP) content using offline reference assays (Chromatography andELISA) and the same samples were subjected to FTIR ATR measurements.Data from offline reference assays and FTIR ATR infrared spectroscopicinformation responses were used to construct chemometric models by usingPLS multivariate data analysis, and the models were cross-validated toassess their accuracy.

Protein A purified, in-house generated-harvest samples of the antibodydrug candidate-X was used for this study. The monoclonal antibody usedwas of the IgG4 subclass and was expressed in a Chinese Hamster Ovary(CHO) mammalian expression system. The harvest samples (26 samples) wereobtained from different culturing days from an alternating tangentialflow perfusion bioreactor. The harvest was purified using a 0.66 cm×20cm I.D. (6.8 mL) Protein A column packed with Mab Select SuRe LX resinusing an AKTA Explorer system (available from GE Healthcare LifeSciences, Pittsburgh, Pa.).

A Bruker MATRIX-MF® FTIR (Bruker Optics, Billerica, Mass.) spectrometerequipped with an IN350T® diamond ATR (attenuated total refection) fiberoptic probe and MCT (Mercury Cadmium Telluride) sensor was used forat-line measurement of samples. About 50 mL of each sample was placed onthe ATR diamond crystal and spectra were measured in the wavenumberrange from 400 cm⁻¹ to 4000 cm⁻¹ (scanning velocity 10 kHz, resolution 2cm⁻¹, and 32 scans per run) using Bruker OPUS acquisition software(available from Bruker Optics, Billerica, Mass.). A reference spectrumwas first recorded using a blank ATR cell on built on probe. Single-beamspectra of all samples were obtained and divided against the backgroundspectrum of air to present the spectra in absorbance units.

The infrared vibrational information was pre-processed and chemometricpartial least-squares (PLS) models for each attribute of interest wereconstructed by using MATLAB computation software (available fromMathWorks, Natick, Mass.) and unscrambler camo software (CRMO SoftwareInc. 33300 Egypt Lane. Magnolia, Tex.). During pre-processing, FTIRspectra were first offset using the average absorbance values between800-1800 cm⁻¹ followed by baseline correction and area normalization.Several pre-processing methods such as Linear Offset Subtraction,Straight Line Subtraction, Vector Normalization, Min-max Normalization,Multiplicative Scatter Correction, First Derivative, and SecondDerivative were evaluated to improve root mean square error ofcross-validation (RMSECV) and coefficient of determination (R²) of thePLS models. Offline chromatography/ELISA-based reference measurements ofeach attribute were correlated with pre-processed FTIR ATR spectral datain constructing PLS models.

To ensure validation of the calibration models, a cross-validationmethod was applied on 20 Protein A purified samples, where each FTIR ATRspectrum of the sample was validated using k-fold cross-validation inwhich the data set was divided into k subsets, and the k-fold model wastrained and tested. Each time, one of the k subsets was used as the testset and the other k−1 subsets were pooled to form a training set. Theaverage errors across all k trials were calculated. The wave number (orfrequency) range that corresponded to each attribute value was optimizedto achieve the best multivariate statistics.

Samples were also tested for antibody concentration, aggregation, chargevariant distribution and host cell protein (HCP) content using offlinechromatography and ELISA assays to generate reference values. Theantibody concentration was measured by Protein A chromatography using a0.21×3 cm I.D. (0.1 mL) POROS Protein A ID cartridge (available fromApplied Biosystems, Foster City, Calif.) on an Agilent 1100 HPLC system(available from Agilent Technologies, Santa Clara, Calif.) followed byUV measurements of the eluates at 280 nm. The percentages of aggregatedforms of the antibody in the samples were analyzed by size exclusionchromatography (SEC) using a 0.78×30 cm I.D. TSKgel G3000SW_(XL)analytical SEC column equipped with a 0.60×4 cm I.D. TSKgel G3000SW_(XL)guard column (available from Tosoh Bioscience, King of Prussia, Pa.) onthe Agilent 1100 HPLC system. A 40 mM sodium phosphate elution buffer in150 mM sodium chloride was used in isocratic mode followed by UVabsorbance at 280 nm using a photodiode array detector. The HCP contentof the samples was measured using a Chinese Hamster Ovary HCP ELISA kitfrom Cygnus technologies (available from Wrentham, Mass.) according tothe manufacturer's manual. Samples were prepared in three dilutionsfollowed by multiple measurements of each.

Charge variant distributions of the antibody in the samples wereanalyzed by capillary isoelectric focusing (cIEF) using iCE3™ system(available from ProteinSimple, San Jose, Calif.). Each sample wasprepared in methyl celluose carrier ampholytes and was allowed toseparate charge variants (Acidic and basic species) on the basis oftheir p1 under electrolytic conditions. The in-built whole-column UVdetector of the iCE3™ system was used to acquire relative distributionof charge variants.

In the infrared spectrum of protein molecule, the chemical structure ofthe molecule is the dominating effect that determines the observedvibrational frequencies via the strengths of the vibrating bonds and themasses of the vibrating atoms. However, it can be difficult tounambiguously determine the chemical structure of a protein based purelyon the infrared spectrum due to the many overlapping bands that aretypically present in the spectrum.

In spite of this, changes in the chemical structure of the molecule canoften be detected based on changes in the observed spectral bands. Onesuch example is the detection of a change in the protonation state ofprotein side chains, which is often essential for protein function. Theprotonation state of many protein side chains is reflected in theinfrared spectrum of the proteins, and can often be reliably deducedfrom infrared spectral information.

The FTIR ATR spectrum of a Protein A purified-antibody sample is shownin FIG. 12, where Amide I (1600-1690 cm′) and Amide II (1480-1575 cm′)characteristic bands are clearly recognizable. The Amide I band, whichis due to C═O stretch vibration of the peptide backbone, is sensitive toα-helix, β-sheet, turn, and unordered conformations of the protein andtheir hydrogen bonding environment. Amide II originates from the N—Hbending and C—N stretching vibrations and it is conformation sensitive.

In some circumstances, the Amide II band corresponds to an out-of-phasecombination of the NH in-plane bending mode and the CN stretching modewith smaller contributions from the CO in-plane bending mode and the CCand NC stretching modes. In proteins, the Amide II band is typicallynearly unaffected by side chain vibrations, but the correlation betweenprotein secondary structure and frequency is less straightforward thanfor the Amide I band, which helps in correlating byproducts of theprotein and provides valuable structural information and secondarystructure predictions for the protein.

The Amide III band between 1300-1400 cm⁻¹ is due to N—H bend in-planeand C—N stretch. Amide IV bands are very complex bands resulting frommixtures of several coordinate displacements mostly arising at 625-725cm⁻¹ due to the O═C—N deformation. The strength of hydrogen bonding inthe secondary structure and coupling between different transitionsdipoles of the peptide influence the absorption frequency in thisregion, and can be used to quantify the protein from experimentalspectroscopic data.

In a biological sample, each conformational entity contributes to amolecule's FTIR ATR spectrum. Amide I band contours are complexcomposites consisting of several overlapping component bands thatrepresent different structural elements such as alpha helices, betasheets, turns, and non-ordered or irregular structures.

To extract compositional structural information encoded in those FTIRbands, FTIR spectroscopic information in frequency ranges from 1100 to1595 cm⁻¹ and 1600 to 1700 cm⁻¹ was used to compute a PLS calibrationusing a second derivative-mean centered FTIR ATR spectra, with exclusionof the absorbance arising from buffer components and water. A PLS modelwas constructed using a k-fold cross-validation method with 20 Protein Apurified samples of antibody drug candidate-X. Overtraining of the modelwas also analyzed and the model robustness was evaluated.

FIG. 13 is a graph showing predicted antibody concentration valuescalculated from the PLS model for antibody concentration, andcorresponding measured antibody concentration values. The PLS modeldemonstrated excellent correlation between FTIR ATR predicted values andoffline chromatography-based reference values. The correlationcoefficient R² was 0.99 and the root mean square error ofcross-validation (RMSECV) was 0.55. The accuracy of the model wasevaluated by predicting the antibody concentration of 6 unknown samplesusing the developed calibration model. Results for the unknown samplesU-1 to U-6 are shown in the table of FIG. 17.

Aggregation phenomena where proteins are mis-folded can lead to shiftingof the key amide peaks in the FTIR ATR spectrum. As a result, aband-narrowing Fourier self-deconvolution technique was used to estimatethe FTIR range and positions of discrete subcomponent absorption bands.The Fourier self-deconvolution decreases band widths, allowingseparation of overlapping component bands using Gaussian functions.

The vibrational frequency of an aggregated protein falls around1620-1625 cm⁻¹ due to the distinct hydrophobic environment, and oftenshows frequency shift of Amide II (˜1540 cm⁻¹). Therefore, the FTIRregions from 1393-1554 cm⁻¹ and 1600-1635 cm⁻¹ were selected to developa calibration model. In addition, the FTIR region from 1180-844 cm⁻¹ wasselected to improve the prediction accuracy due to the C—O—H vibrationsof proteins. FIG. 14A shows an example of the Fourier self-deconvolvedFTIR spectra of Protein A purified samples.

FIG. 14B is a graph showing the PLS calibration model developed foraggregation (solid line) and measured aggregation values (dots). The PLScalibration model demonstrated excellent correlation between FTIRpredicted aggregation values (%) with reference SEC values. The RMSECV,R² and relative percent difference (RDP) values were 0.04, 0.97 and 5.8respectively.

One of the challenges in constructing robust chemometric models for lowconcentration species such as HCPs is the influence frompolyelectrolytes in the medium. It is known that peaks appearing atapproximately 1,390 cm⁻¹ and 1005-110 cm⁻¹ can be assigned topolyelectrolytes. FIG. 15A shows a set of mean-centered,baseline-corrected, and area-averaged second derivative FTIR ATR spectraused for the construction of a PLS model for HCPs.

To develop the PLS model for HCP quantitative determination, mid-IRregion frequency ranges from 1500-1600 cm⁻¹, 1600-1680 cm⁻¹, 1489-1414cm⁻¹, and 1174-1286 cm⁻¹ were used. All of the spectra weremean-centered, baseline-corrected, and area-averaged, as shown in FIG.15A.

FIG. 15B is a graph showing the model for HCP quantitative determination(solid line) and measured HCP values (dots). A fairly good correlationis observed, with R²=0.89, RMSECV=71.1, and RPD=3.05.

To evaluate PLS models for determining the charge variant distribution,pre-processed FTIR ATR spectra of the samples were subjected to k-foldcross-validation using 20 samples for pre-Main (acidic), Main, andpost-Main (basic) species to construct three separate models,respectively. Overtraining and robustness of the models were evaluated.The analysis of charge variants depends largely on C-terminal lysinemodifications, which lead to spectrum pattern shift mostly in thefingerprint region from 1000 to 1850 cm⁻¹. Accordingly, mean-centeredspectra subjected to multiple scatter corrections and second derivativeanalysis from 1118 cm⁻¹ to 1500 cm⁻¹ were used for the construction ofthe PLS model for the Main peak, which is shown (solid line) in FIG.16B. PLS statistics for the Main peak model include R² of 0.99, RMSECVof 0.00,1 and RPD of 12.4.

For the pre-Main analysis, the FTIR region from 1120 cm to 1470 cm⁻¹yielded the best PLS calibration model, using PLS rank up to 10. Themodel is shown FIG. 16A. The RMSECV, R², and RPD values were 0.00125,0.9937, and 12.6 respectively.

For the post-Main analysis, the FTIR region from 1187 cm⁻¹ to 1839 cm⁻¹was used to construct the PLS model, which is shown on the graph of FIG.16C, An excellent correlation with measured values from the referencecIEF method was achieved. The RMSECV, R² and RPD values were 0.00118,99.59, and 15.7. The accuracy of the models was also evaluated bypredicting the charge variant distribution of 6 unknown samples, U-1 toU-6, the results of which are shown in FIG. 17.

To evaluate the effectiveness of using in-line infrared spectralmeasurements and chemometric models to accurately determine values ofbioreactor medium components and conditions, the methods disclosedherein were applied to the measurement of glucose concentration,glutamine concentration, IgG concentration, lactate concentration,ammonia concentration, and osmolarity. Specifically, in-line ATRinfrared spectral measurements were performed on the reactor medium, andvalidated chemometric models for each of these quantities were used topredict values for each of the quantities from the infrared spectralmeasurements. To determine the accuracy of the infrared-derived values,the reactor medium was also manually sampled and values of each of thequantities was determined via individual analysis.

FIGS. 21A-21F are graphs showing “true” values (diamond markers) andpredicted (or infrared-derived) values (solid line) of glucoseconcentration (FIG. 21A), glutamine concentration (FIG. 21B), IgGconcentration (FIG. 21C), lactate concentration (FIG. 21D), ammonium ionconcentration (FIG. 21E), and osmolarity (FIG. 21F) for the bioreactormedium. Excellent agreement between the true and predicted values wasachieved for each measured quantity.

FIGS. 22A-22D are graphs showing true (diamond markers) and predicted(dot markers) values of glucose concentration (FIG. 22A), harvest titer(FIG. 22B), lactate concentration (FIG. 22C), and ammonium ionconcentration (FIG. 22D). For each true value, multiple predicted valueswere determined from multiple infrared spectral measurements, and thedistribution of dot markers in FIGS. 22A-22D indicate the variability ofthe values predicted based in infrared spectral information. Although arelatively small number of outliers appear in each of the figures, mostof the individually predicted values are in close agreement with thetrue values of the various quantities, and the distributions of each setof infrared-determined values is in agreement with the correspondingtrue value.

FIGS. 23A-23C are graphs showing true (large dots) and predicted (i.e.,infrared-determined, small dots) values of glucose concentration (FIG.23A), lactate concentration (FIG. 23B), and ammonium ion concentration(FIG. 23C) at later stages of a bioreactor culture medium, in days 42and 43. As is evident from the graphs, the infrared-determined values ofthese quantities continue to agree well with the true values,demonstrating that chemometrics-based methods that rely on infraredspectral information to determine values of multiple different mediumcomponent-related quantities remain highly accurate, even as the cellculture and medium continue to yield products after a relatively longtime.

What is claimed is:
 1. A method, comprising: obtaining a vibrational spectrum of a solution in a biological manufacturing system; analyzing the vibrational spectrum using a first chemometrics model to determine a value of a first quality attribute associated with the solution; analyzing the vibrational spectrum using a second chemometrics model to determine a value of a second quality attribute associated with the solution; and adjusting at least one parameter of a purification unit of the biological manufacturing system based on at least one of the values of the first and second quality attributes.
 2. The method of claim 1, further comprising using the biological manufacturing system to produce at least one of: a protein-based therapeutic substance comprising at least one of a protein, a peptide, an antibody, and an enzyme; a nucleic acid-based drug substance comprising at least one of DNA, a plasmid, an oligonucleotide, an aptamer, a DNAzyme, an RNA aptamer, an RNA decoy, a microRNA fragment, and a small interfering RNA fragment; and a gene therapy drug substance.
 3. The method of claim 1, wherein the first and second quality attributes are each independently selected from the group consisting of product quality attributes, product-related impurities, and process-related impurities, for a biological product produced by the biological manufacturing system.
 4. The method of claim 1, wherein obtaining the vibrational spectrum comprises directing radiation to be incident on the solution and measuring attenuated totally reflected radiation from the solution.
 5. The method of claim 4, further comprising measuring the attenuated totally reflected radiation from the solution while the solution is flowing relative to a radiation window.
 6. The method of claim 1, wherein the first chemometrics model comprises a first set of principal vibrational components correlated with the first quality attribute.
 7. The method of claim 6, wherein analyzing the vibrational spectrum using the first chemometrics model comprises calculating the first quality attribute value based on the first set of principal vibrational components.
 8. The method of claim 7, wherein the first and second sets of principal vibrational components have no members in common.
 9. The method of claim 1, wherein the solution comprises a solution discharged from a purification unit of the biological manufacturing system.
 10. The method of claim 9, further comprising purifying the solution in the purification unit prior to obtaining the vibrational spectrum of the solution.
 11. The method of claim 1, further comprising obtaining the vibrational spectrum by measuring radiation from the solution as the solution flows between a first purification unit and a second purification unit of the biological manufacturing system.
 12. The method of claim 1, further comprising obtaining the vibrational spectrum by measuring radiation from the solution after the solution flows out of a final purification unit of the biological manufacturing system.
 13. The method of claim 1, wherein the solution is a first solution, the method further comprising: obtaining a vibrational spectrum of a second solution in the biological manufacturing system; analyzing the vibrational spectrum of the second solution using the first chemometrics model to determine a value of the first quality attribute for the second solution; and analyzing the vibrational spectrum of the second solution using the second chemometrics model to determine a value of the second quality attribute for the second solution, wherein the first solution flows between a first purification unit and a second purification unit of the biological manufacturing system, and the second solution flows between the second purification unit and a third purification unit of the biological manufacturing system.
 14. The method of claim 13, further comprising adjusting the at least one parameter based on at least one of the first and second quality attribute values for the first solution, and the first and second quality attribute values for the second solution.
 15. The method of claim 1, wherein at least one of the first and second quality attributes is selected from the group consisting of concentration, aggregates, charge variant distribution, purity, glycan profile, identity, integrity, protein fragments, nucleic acid fragments, nucleic acid variants, empty capsids, vector impurities, host cell proteins, residual host DNA, residual column ligands, impurity concentration, impurity amount, residual helper virus, residual helper viral proteins, and residual helper viral DNA.
 16. A biological manufacturing system, comprising: a bioreactor configured to produce a solution comprising a biological product; a purification unit configured to receive the solution; a radiation source configured to direct radiation to be incident on the solution; a detection apparatus configured to measured radiation from the solution; and a system controller connected to the bioreactor and the detection apparatus, and configured to: receive a measurement signal from the detection apparatus corresponding to information about a vibrational spectrum of the solution; analyze the information using a first chemometrics model to determine value of a first quality attribute associated with the solution; analyze the information using a second chemometrics model to determine a value of a second quality attributed associated with the solution; and adjust at least one parameter of the purification unit based on at least one of the values of the first and second quality attributes.
 17. The system of claim 16, further comprising a flow cell positioned so that the solution passes through the flow cell, and the radiation source directs the radiation to be incident on the solution while the solution is in the flow cell.
 18. The system of claim 16, wherein the biological product comprises at least one of: a protein-based therapeutic substance comprising at least one of a protein, a peptide, an antibody, and an enzyme; a nucleic acid-based drug substance comprising at least one of DNA, a plasmid, an oligonucleotide, an aptamer, a DNAzyme, an RNA aptamer, an RNA decoy, a microRNA fragment, and a small interfering RNA fragment; and a gene therapy drug substance.
 19. The system of claim 16, wherein the first and second quality attributes are each independently selected from the group consisting of product quality attributes, product-related impurities, and process-related impurities, for a biological product produced by the biological manufacturing system.
 20. The system of claim 17, wherein the detector comprises a total internal reflection sensor configured to measure attenuated totally reflected radiation from the solution.
 21. The system of claim 20, wherein the total internal reflection sensor is integrated with a portion of the flow cell.
 22. The system of claim 16, wherein the first chemometrics model comprises a first set of principal vibrational components correlated with the first quality attribute.
 23. The system of claim 22, wherein the controller is configured to analyze the vibrational spectrum using the first chemometrics model by calculating the first quality attribute value based on the first set of principal vibrational components.
 24. The system of claim 22, wherein the second chemometrics model comprises a second set of principal vibrational components correlated with the first quality attribute.
 25. The system of claim 24, wherein the first and second sets of principal vibrational components have no members in common.
 26. The system of claim 24, wherein the controller is configured to analyze the vibrational spectrum using the second chemometrics model by calculating the second quality attribute value based on the second set of principal vibrational components.
 27. The system of claim 16, wherein the solution comprises a solution discharged from the purification unit, and wherein the controller is connected to the purification unit and configured to purify the solution in the purification unit prior to obtaining the information about the vibrational spectrum of the solution.
 28. The system of claim 16, wherein the purification unit is a first purification unit of the system, and the system further comprises: a second purification unit configured to receive the solution, wherein the detection apparatus is positioned to measure radiation from the solution as the solution flows between the first purification unit and the second purification unit.
 29. The system of claim 28, wherein the first and second purification units each comprise a chromatography column.
 30. The system of claim 16, wherein the purification unit is a final purification unit of the system. 